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Novel Approaches to Evaluating and Characterizing Force Sensor Performance at Body-Device Interfaces

by

Megan Hamilton

A thesis submitted in conformity with the requirements for the degree of Master of Health Science in Clinical Engineering

Institute of Biomaterials & Biomedical Engineering University of Toronto

© Copyright by Megan Hamilton 2019

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Novel Approaches to Evaluating and Characterizing Force Sensor

Performance at Body-Device Interfaces

Megan Hamilton

Master of Health Science in Clinical Engineering

Institute of Biomaterials & Biomedical Engineering

University of Toronto

2019

Abstract

Force and pressure sensors are used to monitor fit and inform designers, researchers, and

clinicians in various biomedical applications. However, sensor performance at the body-device

interface is not well understood. The objective of this thesis was to develop and apply novel

approaches to evaluating force sensor performance at clinically-relevant body-device interfaces.

This work includes the characterization of existing transducers as well as the development of a

novel sensor for assistive device applications. Clinically-relevant static and dynamic loading

profiles were applied to two commercial sensors and the prototype sensor via a mechanical

testing machine and an apparatus simulating the lower limb. The results of this study provide

recommendations for use and summarize the effects of area of applied load, sensor

configuration, tissue compliance, and calibration methods. This integration of force sensors is a

vital first step towards adaptable, intelligent assistive devices that have the potential to improve

fit, comfort, and performance.

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Acknowledgments

This work was made possible due to the support from a number of individuals, to which I am

deeply indebted.

First and foremost, I would like to thank my supervisor, Dr. Jan Andrysek, for his continuous

support and leadership these past few years. For this, I am extremely grateful. I would also like

to express my appreciation to my co-supervisor, Dr. Kamran Behdinan, and the members of my

thesis supervisory committee, Dr. Eric Diller, Dr. Karl Zabjek, and Dr. Kei Masani, for their

insights, guidance, and dedication in steering this project.

I would also like to express my gratitude to Neil Ready from the Prosthetics and Orthotics

Department at Holland Bloorview Kids Rehabilitation Hospital, for his illuminating feedback

and generous support in developing the methodology.

To the members of the PROPEL Lab I had the privilege and pleasure of working with over the

past few years: Dr. Matthew Leineweber, Dr. Arezoo Eshraghi, Rafael, Calvin, Brock, Mark,

Sam S., Rachel, Emerson, Alex, Firdous, and Harry, thank you for the endless support and

endless laughter. I am grateful to have worked with so many bright, caring, and passionate

researchers. Sam W., thank you as well for your contributions to this study.

A special thanks goes out to Dr. Sharvari Dhote of the ARL-MLS Lab for her assistance in the

study development.

Finally, I wish to express my deepest appreciation and heartfelt gratitude to my loved ones – my

friends and family both near and far – the completion of this work would not have been possible

without you. To my parents, Sara, Penny, and Matt: thank you for your unwavering support; it

means more to me than you know.

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Table of Contents

Acknowledgments.......................................................................................................................... iii

Table of Contents ........................................................................................................................... iv

List of Tables ................................................................................................................................ vii

List of Figures .............................................................................................................................. viii

List of Abbreviations ..................................................................................................................... xi

List of Foundations and Funding Sources .................................................................................... xii

Chapter 1 ..........................................................................................................................................1

Introduction .................................................................................................................................1

1.1 Thesis Outline ......................................................................................................................1

1.2 Background and Rationale ...................................................................................................1

1.3 Statement of Research Objectives .......................................................................................4

Chapter 2 ..........................................................................................................................................5

Literature Review ........................................................................................................................5

2.1 Sensing in MAT Applications .............................................................................................5

2.1.1 Types of Transducers for Pressure Measurement ....................................................6

2.1.2 Sensor Evaluation Studies......................................................................................10

2.1.3 Sensing Configurations ..........................................................................................11

2.2 Pressure Distributions at the Body-Device Interface .........................................................12

2.2.1 Pressure Distributions During Gait ........................................................................12

2.2.2 Normal and Shear Loading ....................................................................................14

2.3 Additive Manufacturing .....................................................................................................14

2.3.1 3D-Printed Assistive Devices ................................................................................14

2.3.2 3D-Printed Pressure Sensors ..................................................................................15

Chapter 3 ........................................................................................................................................16

Commercial Sensor Evaluation .................................................................................................16

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3.1 Load Area and Sensor Configuration ................................................................................16

3.1.1 Introduction ............................................................................................................16

3.1.2 Methods..................................................................................................................18

3.1.3 Results ....................................................................................................................22

3.1.4 Discussion and Conclusions ..................................................................................27

3.2 Tissue Compliance .............................................................................................................30

3.2.1 Introduction ............................................................................................................30

3.2.2 Methods..................................................................................................................31

3.2.3 Results ....................................................................................................................33


3.3 Dynamic Performance .......................................................................................................41

3.3.1 Introduction ............................................................................................................41

3.3.2 Methods..................................................................................................................42

3.3.4 Results ....................................................................................................................46


Chapter 4 ........................................................................................................................................54

QTC Prototype Sensor Evaluation ............................................................................................54

4.1 Introduction ........................................................................................................................54

4.1.1 Sensor Fabrication .................................................................................................54

4.2 Methods..............................................................................................................................55

4.2.1 Testing Apparatus ..................................................................................................55

4.2.2 Protocol ..................................................................................................................56

4.2.3 Analysis..................................................................................................................56

4.3 Results ................................................................................................................................56

4.3.1 Calibration..............................................................................................................56

4.3.2 Dynamic Hysteresis Testing ..................................................................................58

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4.3.3 Dynamic Simulated Gait Testing ...........................................................................63

4.4 Discussion and Conclusions ..............................................................................................65

Chapter 5 ........................................................................................................................................67

Discussion and Conclusions ......................................................................................................67

5.1 Limitations and Future Work .............................................................................................68

5.2 Significance........................................................................................................................70

References ......................................................................................................................................71

Appendices .....................................................................................................................................77

Appendix A: Explaining the Deadband in Hysteresis Data ......................................................77

Appendix B: QTC Prototype Sensor Inter-Sample Repeatability.............................................78

Appendix C: Additional Data ...................................................................................................79

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List of Tables

Table 3-1: Commercial sensor model specifications. ................................................................... 18

Table 3-2: Application conditions: Combinations of variables tested .......................................... 20

Table 3-3: Coefficient of variation values by area and configuration. ......................................... 25

Table 3-4: ANOVA results (p-values) from generalized-area calibration data. ........................... 25

Table 3-5: Specifications for three compliant materials tested. .................................................... 31

Table 3-6: Coefficient of variation values for configuration and material for Peratech and

Sensitronics sensors using matched-area, generalized-area and across configuration calibration

methods. ........................................................................................................................................ 37

Table 3-7: ANOVA results (p-values) from the factorial analysis performed on each sensor

model using generalized-area calibration data. ............................................................................. 37

Table 3-8: Hysteresis error and NRMSE from hysteresis test using both matched-area and

generalized-area calibration for both Peratech and Sensitronics sensors. .................................... 49

Table 3-9: ANOVA results (p-values) for both Peratech and Sensitronics sensors from hysteresis

test results...................................................................................................................................... 49

Table 3-10: Gait simulation testing error values for both Peratech and Sensitronics sensors. ..... 51

Table 3-11: ANOVA results (p-values) from gait simulation testing results. .............................. 51

Table 4-1: Coefficient of variation values for 16 application conditions. .................................... 58

Table 4-2: ANOVA results (p-values) using both matched-area and

generalized-area calibration data. ................................................................................................. 58

Table 4-3: Hysteresis error and NRMSE from two and fifteen-second hysteresis tests using both

matched-area and generalized-area calibration methods. ............................................................. 63

Table 4-4: Gait simulation testing error values for QTC prototype sensor. ................................. 65

Table A-1: CV values calculated for each material, configuration and area for both Peratech and

Sensitronics sensors, using both matched-area and generalized-area calibration methods. ......... 79

viii

List of Figures

Figure 2-1: Strain gauge-based transducers on a prosthetic socket [38]. ....................................... 6

Figure 2-2: Commercially available thin-film interfacial pressure sensors (left to right): Peratech

QTC™, Interlink FSR ®, Sensitronics ® FSR, Tactilus Free Form®, and Tekscan FlexiForce®

A301 [31]. ....................................................................................................................................... 7

Figure 2-3: Apparatus used by Pirouzi et al. to evaluate socket fitting using Tekscan F-Scan

pressure measurement arrays covering the residual limb [28]. ....................................................... 8

Figure 2-4: Experimental apparatus used by Jensen et al [46]. .................................................... 11

Figure 2-5: Loading configuration with and without an elastomer puck highlighting the

difference in effective loading area when the area of applied load is larger than the FSR’s sensing

area. ............................................................................................................................................... 12

Figure 2-6: Typical walking gait phases [47]. Stance and swing phases are labelled for one gait

cycle on the highlighted (right) leg. .............................................................................................. 13

Figure 2-7: Typical interface pressure profile at the residual limb-socket interface of a

transtibial amputee during one stride [48]. ................................................................................... 13

Figure 3-1: Peratech (top) and Sensitronics (bottom) sensors. ..................................................... 18

Figure 3-2: Actual setup (left) and labelled schematic (right) showing configuration with both

loading puck and rigid backing. .................................................................................................... 19

Figure 3-3: Characteristic resistance vs. force curve for the Peratech sensor under all 16

application conditions. .................................................................................................................. 23

Figure 3-4: Characteristic resistance vs. force curve for the Sensitronics sensor under all 16

application conditions. .................................................................................................................. 23

Figure 3-5: Interaction profiles for Peratech sensor using matched-area calibration data. .......... 26

Figure 3-6: Interaction profiles for Sensitronics using matched-area calibration data. ................ 26

Figure 3-7: Characteristic resistance vs. force curves from calibration of compliance testing for

Peratech sensor.............................................................................................................................. 35


Sensitronics sensor. ....................................................................................................................... 36

Figure 3-9: Interaction profiles for Peratech sensor using simplified generalized-area calibration

CV values. ..................................................................................................................................... 38

Figure 3-10: Interaction profiles for Sensitronics sensor using simplified generalized-area

calibration CV values. ................................................................................................................... 38

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Figure 3-11: Force vs. time plots for two-second hysteresis tests for Peratech sensor using

matched-area (subplots i and ii) and generalized-area (subplots iii and iv) calibration methods. 47

Figure 3-12: Force vs. time plots for two-second hysteresis tests for Sensitronics sensor using

matched-area (subplots i and ii) and generalized-area (subplots iii and iv) calibration methods. 48

Figure 3-13: Force measured vs. force applied plots for two-second hysteresis tests for Peratech

sensor using specific matched-area (subplots i and ii) and simplified generalized-area (subplots

iii and iv) calibration methods. ..................................................................................................... 48

Figure 3-14: Force measured vs. force applied plots for two-second hysteresis tests for

Sensitronics sensor using specific matched-area (subplots i and ii) and simplified generalized-

area (subplots iii and iv) calibration methods. .............................................................................. 49

Figure 3-15: Force vs. time plots from dynamic gait testing tests for Peratech sensor using

specific matched-area (subplots i and ii) and simplified generalized-area (subplots iii and iv)

calibration methods. ...................................................................................................................... 50

Figure 3-16: Force vs. time plots from dynamic gait testing tests for Sensitronics sensor using



Figure 4-1: Testing apparatus for QTC prototype sensor. ............................................................ 55

Figure 4-2: Characteristic resistance vs. force curves from QTC prototype sensor calibration

using 16 application conditions. ................................................................................................... 57

Figure 4-3: Force vs. time plots for two-second hysteresis tests for QTC prototype sensor using



Figure 4-4: Force measured vs. force applied plots for two-second hysteresis tests for QTC

prototype sensor using specific matched-area (subplots i and ii) and simplified generalized-area

(subplots iii and iv) calibration methods. ...................................................................................... 60

Figure 4-5: Force vs. time plots for fifteen-second hysteresis tests for the QTC prototype sensor

using matched-area calibration method. ....................................................................................... 61

Figure 4-6: Force vs. time plots for fifteen-second hysteresis tests for QTC prototype sensor

using generalized-area calibration method. .................................................................................. 61

Figure 4-7: Force measured vs. force applied plots for fifteen-second hysteresis tests for QTC

prototype sensor using matched-area calibration method. ............................................................ 62

Figure 4-8: Force measured vs. force applied plots for fifteen-second hysteresis tests for the QTC

prototype sensor using generalized-area calibration method. ....................................................... 62

Figure 4-9: Force vs. time plots from dynamic gait testing calculated using matched-area

calibration data. ............................................................................................................................. 64

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Figure 4-10: Force vs. time plots from dynamic gait testing calculated using generalized-area

calibration data. ............................................................................................................................. 64

Figure A-1: Raw data and fitted curve for calibration data showing no collected data below 3.5

N. ................................................................................................................................................... 77

Figure A-2: Raw resistance, calculated force, and applied force waveforms displaying deadband.

....................................................................................................................................................... 77

Figure A-3: Characteristic resistance vs. force curve for two QTC prototype sensor samples. ... 78

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List of Abbreviations

3D Three dimensional

AM Additive manufacturing

ANOVA Analysis of variance

CB Carbon black

CV Coefficient of variation

DMM Digital multimeter

FSR Force-sensing resistor

GA Generalized-area (calibration method)

HE Hysteresis error

MA Matched-area (calibration method)

MAT Mobility assistive technology

NBNP No rigid backing or elastomer puck (configuration)

NBYP No rigid backing with elastomer puck (configuration)

NRMSE Normalized root mean square error

PDMS Polydimethylphenylsiloxane

PET Polyethylene terephthalate

PLA Polylactic acid

QTC Quantum tunneling composite

RMSE Root mean square error

SD Standard deviation

YBNP Rigid backing with no elastomer puck (configuration)

YBYP Rigid backing with elastomer puck (configuration)

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List of Foundations and Funding Sources

EMHSeed, University of Toronto

Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery

RGPIN 2018-05046

Queen Elizabeth II/Victoria Noakes Graduate Scholarship in Science and Technology,

University of Toronto

Barbara and Frank Milligan Award, University of Toronto

Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital

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Chapter 1

Introduction

1.1 Thesis Outline

The following document consists of five chapters. Chapter 1, beginning here, provides the

rationale for pressure sensing in assistive devices and summarizes the limited understanding of

commercial sensor performance at the body-device interface. Next, the study objectives are

explicitly stated, building from the background and rationale presented.

Chapter 2 provides a survey of the literature on the areas most relevant to the research objectives,

including sensing in mobility assistive technology applications, pressure distributions at the

body-device interface, and a brief description of additive manufacturing of assistive devices and

pressure sensors.

Chapter 3 presents the evaluation of two commercial interfacial pressure sensors using the

methods developed. The three sub-sections outline three different sub-studies: 3.1 examines the

effects of load area and sensor configuration using a static loading protocol. This section was

submitted as a manuscript, including introduction, methods, discussion, and conclusion sections.

Section 3.2 builds off 3.1, with the addition of an advanced calibration loading protocol and

study of an additional experimental factor: tissue compliance. Lastly, Section 3.3 examines the

same commercial sensors using a novel dynamic loading protocol.

Chapter 4 presents the evaluation of a QTC prototype sensor under development, using the

methods previously described in Chapter 3. The sensor is compared to the commercial sensors.

To conclude, Chapter 5 summarizes the main findings and discussion points. Contributions to the

field, limitations to the study, and future directions are also described.

1.2 Background and Rationale

Individuals with physical disabilities can lead fulfilling, independent lives owing to the advances

in wearable sensors and mobility assistive technology (MAT), including prosthetics and

orthotics. The prevalence and financial burden of MAT is evident from the statistical reports; in

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2016, Medicare approved payment for nearly 3.04 million orthotic services and 2.11 million

prosthetic services, totaling expenditures of USD $1.0 billion and $717 million, respectively [1].

The success of MAT devices, including prosthetics and orthotics, is dependent on their function,

fit, and comfort. There are several negative consequences to a poorly fitting MAT device:

discomfort; excessive stresses, skin irritation, breakdown, and ulcers [2]–[4]; compromised

mobility [5]; and the individual’s decision to discontinue device use [5]. In individuals with

limb-loss, the fit of the socket has been cited as the single most important parameter affecting

user satisfaction [5]. It is also understood that the fit of the socket is dependent on the pressure

distribution at the body-device (i.e. socket) interface [5].

Quantifying the pressure distributions at the body-device interface can help inform the design,

prescription, and fitting of patient-customized MAT devices like prosthetics and orthotics.

Current MAT device fittings require a subjective, manual process dependent primarily on the

experience of the clinician. Reducing the subjectivity in fittings has the potential to improve

device fit, comfort, and usability [6]. In individuals with limb-loss, the body-device interface is

affected by both short and long-term changes, such as prosthesis alignment and setup, growth or

atrophy of the residual limb, and medical complications such as skin lesions, rashes, or pressure

sores. Adjustments must be made by certified prosthetists to accommodate for these changes.

MAT devices incorporating pressure sensors can provide continuous monitoring of the body-

device interface, and enable early detection of conditions that may require clinical intervention

[2], [7], [8].

Force and pressure sensors have been used in a variety of biomedical applications at the body-

device interface, however there is limited data regarding sensor performance in such applications

[9]. Commercial force-sensing resistors (FSR) manufacturers state that consistent actuation and a

rigid surface beneath the sensor are required to ensure repeatable measurement results. When the

area of applied is load is larger than the sensor, manufacturers recommend a thin elastomer

slightly smaller than the sensing diameter, also known as a load “puck”, be placed between the

sensor and load applicator to ensure the force is distributed evenly over the sensing area [10].

However, the body-device interface is subjected to dynamically changing actuation, and the

presence of rigid substrates can cause pain and affect the pressure distribution. Additionally,

commercial FSRs have demonstrated several limitations, including accuracy errors and high

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hysteresis effects [4], as well as sensitivity to calibration methods and loading conditions [9].

Interfacial sensors require a thin and flexible substrate to minimize effects (i.e., abnormal

pressure distributions) in the system, and a simple manufacturing process that enables integration

into clinical devices [11]–[13].

Due to the complex nature of the body-device interface, it is not feasible to match calibration

conditions to experimental use. For example, the area of applied load will fluctuate throughout

use due to both dynamic movements during gait and changes in the residual limb over time.

Technically, manufacturers would recommend re-calibrating with each area and configuration

that is applied to the sensor – a task that is neither practical nor necessarily possible. There is

thus the need for a simplified, generalized-area calibration method, in-contrast to the traditionally

recommended matched-area calibration method.

Current patient-customized MAT devices require manual manufacturing that is neither time nor

cost effective. These devices do not incorporate pressure sensing due to limitations in sensor

accuracy and ease of integration [14], [15]. Additive manufacturing (AM), commonly referred to

as 3D-printing, shows high potential for the quick and low-cost production of customized

devices with integrated sensors; however, the application of 3D-printing in pressure sensor

development requires further investigation [16]–[18]. There is currently no 3D-printed pressure

sensor available for monitoring body-device interfaces in clinical applications, as no sensors

have demonstrated the performance nor the ease of integration required [12], [18]. The

incorporation of 3D-printing into the fabrication of sensors and assistive devices will produce

cost-effective, comfortable, and intelligent interfaces with the potential to revolutionize the

industry.

A pressure sensor designed to be 3D-printed and embedded within MAT is being developed by

the ARL-MLS (Advanced Research Laboratory for Multifunctional Lightweight Structures) and

PROPEL (Paediatrics, Rehabilitation, Orthotics, Prosthetics, Engineering, Locomotion) labs at

the University of Toronto. The device is a quantum tunneling composite (QTC) pressure sensor

in the initial stages of design and development. The current sensing prototype is fabricated using

manual methods, and its performance is yet to be fully explored. Upon validation of this sensor

prototype, the two labs will begin investigating 3D-printing this sensor (outside the scope of this

study).

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To validate pressure and force measurements at the body-device interface, there is consequently

a need for a standardized, repeatable method to evaluate and characterize pressure sensor

performance under clinically relevant conditions (i.e., simulating MAT interfaces). This

evaluation should examine multiple factors, including area of applied load, sensor configuration,

and calibration method. This evaluation is needed for both sensors that have been

commercialized and those under development.

1.3 Statement of Research Objectives

The overall objective of this thesis was to develop and apply novel approaches to evaluating and

characterizing pressure sensor performance at clinically relevant body-device interfaces.

The primary aims of this work were as follows:

I. Development of a testing protocol that determines the effects of multiple relevant

parameters and simulates clinically relevant body-device interfaces.

i. Static evaluation of the effects of load area, sensor configuration, and tissue

compliance.

ii. Dynamic evaluation of the effects of load area and sensor configuration.

iii. Examination of the effects of two calibration techniques on sensor performance,

including calibrating under matched-area (i.e. specific) and generalized-area (i.e.

simplified) conditions.

II. Evaluation of both commercially available sensors and those under development.

i. Evaluation and comparative analysis across top-performing commercial FSRs.

ii. Evaluation of the QTC prototype sensor.

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Chapter 2

Literature Review

2.1 Sensing in MAT Applications

Advances in wearable sensors and sensing technology have the potential to transform traditional

MAT devices into smart-sensing, intelligent devices that can detect and respond to changes in

the user’s environment. Sensing can be used in MAT applications to enable real-time

physiological monitoring, advanced diagnostics, and integrated sensory feedback [18]. Many

different sensors have been developed for use in MAT, including force/pressure [12], strain [19],

tactile [20], displacement[21], angular [22], flow [23], electroencephalography (EEG)[24],

temperature and humidity-monitoring sensors [25], biosensors [26] and accelerometers [18],

[24].

Pressure sensing plays a particularly important role in MAT devices. The results can help inform

the design, prescription, and fitting of patient-customized MAT devices like prosthetics and

orthotics. Current MAT device fittings require a subjective, manual process. Reducing the

subjectivity in a manual fitting has the potential to improve device fit, comfort, and usability [6].

Continuous monitoring can also enable early detection of conditions that may require clinical

intervention, including skin lesions and pressure sores arising from abnormal pressure

distributions [2], [7], [8].

A limited number of studies have been performed to attempt to reduce the subjectivity in

prosthetic fitting, with the objective of reducing the time for both the clinician and the patient to

achieve the best fit, as well as improving the comfort for the patient [5], [27]. One study,

performed by Dakhil et al., demonstrated a correlation between the pain score of patients and

recorded pressures at the body-device interface [27]. The researchers developed a simple,

repeatable clinical protocol using pressure sensors that quantified the pressure distribution at the

body-device interface during both standing and walking trials to improve feedback for prosthesis

fitting and design [27]. Another study, performed by Sewell et al., developed a methodology

using an artificial intelligence approach, specifically inverse problem analysis, to determine

pressures that the body-device interface during standing and walking [5]. The approach

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calculated the interfacial pressures based off pressure sensors on the exterior of the socket, and

showed promising results, however it is still under development.

2.1.1 Types of Transducers for Pressure Measurement

Pressure sensors have been used in a variety of biomedical applications at the body-device

interface, however there is limited data regarding sensor performance in such applications [9].

Applications include, but are not limited to, prosthetic and assistive devices [12], [28],

laparoscopic instruments [29], tourniquets [30], compression garments [31], helmets [32], in-

shoe gait analysis devices [33], and robotics [34]. A successful sensing element could therefore

be used in a broad variety of applications.

A variety of measurement techniques and sensors have been developed to monitor pressures at

body-device interfaces in order to improve fit and comfort for the user [35]. These sensors

include strain gauges, piezoresistive single-point sensors, piezoresistive sensor arrays, and

capacitive sensors [4].

Strain gauges are a commonly used sensing element that provide high sensitivity and accuracy.

Strain gauges feature small patches of material (i.e., metal) that exhibit a change in electrical

resistance in response to an applied load, and are usually housed in bulky cylindrical shells [4].

However, strain gauges were not designed for measurement at the body-device interface in

clinical settings. Due to their bulkiness, they require permanent modification to an MAT device

for integration (i.e., holes drilled into socket wall to support the strain-gauge and ensure the

sensor is flush against the body) [36]. Another limitation for clinical use is that the strain gauges

have relatively high power requirements for operation; batteries are required for short-term use

[37].

Figure 2-1: Strain gauge-based transducers on a prosthetic socket [38].

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Piezoresistive sensors are often used for interface pressure measurement due to their thin,

flexible construction, adequate sensitivity, and ease of use [31], [39]. These sensors act as

variable resistors in a circuit: the conductivity of the sensor increases when subjected to an

applied load, as the contact between the conductive particles in the sensor’s polymer matrix

increases. There are several commercially available single point piezoresistive pressure sensors,

displayed in Figure 2-2, including the Quantum Tunneling Composite (QTC™) sensor (Peratech

Ltd, Richmond, North Yorkshire, UK); the FSR® (Interlink Electronics Inc, Westlake Village,

CA, USA); the Tactilus Free Form® sensor (Sensor Products Inc., Madison, NJ, USA); and the

FlexiForce® sensor (Tekscan, Boston, MA, USA). These sensors are often referred to as force-

sensitive resistors (FSRs). One limitation of these sensors is that they measure pressures at a

single point only. For pressure-mapping purposes over a greater area, many single-point sensors

are required, and pressures must be extrapolated at areas between sensors.

Figure 2-2: Commercially available thin-film interfacial pressure sensors (left to right): Peratech

QTC™, Interlink FSR ®, Sensitronics ® FSR, Tactilus Free Form®, and Tekscan FlexiForce®

A301 [31].

Piezoresistive sensing arrays have also been developed to attempt to map pressure distributions

over largers areas, including use within prosthetic sockets. They feature a thin, flexible substrate

and high spatial resolution. The two main commercially available piezoresistive pressure sensing

arrays include Rincoe Socket Fitting (RG Rincoe and Associates, Golden, CO, USA) and F-

Socket (Tekscan, Boston, MA, USA), the latter shown in Figure 2-3. Both piezoresistive sensing

arrays and single-point sensors do not require modification for use in sockets or at other body-

device interfaces, making them a much more viable clinical tool compared to strain gauges.

However, these sensors are susceptible to high hysteresis effects [4].

8

Figure 2-3: Apparatus used by Pirouzi et al. to evaluate socket fitting using Tekscan F-Scan

pressure measurement arrays covering the residual limb [28].

Capacitance-based sensors are constructed of a dielectric material inserted between two parallel

conductive surfaces. The capacitance is a function of the overlapping surface area between the

two conductive surfaces, as well as the distance between them [40]. These sensors have high

precision; however, they are often constructed of rigid substrates and are therefore not suitable

for measurement at body-device interfaces [4]. Laszczak et al. are developing a capacitance-

based sensor, designed for monitoring stresses at the residual limb-socket interface of lower-limb

amputees. The sensor measures both pressure and shear stresses and is constructed of a flexible

3D-printed substrate. The sensor has shown promising preliminary results, but significant

development is required before a product is realized for clinical use [12].

Of the sensors described, single-point piezoresistive sensors, or FSRs, are the most applicable for

monitoring pressure at the body-device interface due to their thin, flexible substrate and small

size that can be tailored to many different applications.

2.1.1.1 Thin-Film Sensors for Interface Pressure Measurement

This section describes the sensor composition and operational principles behind three of the top-

performing thin-film interfacial pressure sensors: the Peratech QTC™ , the Sensitronics

ThruMode™ FSR, and the Tekscan FlexiForce®, as determined by studies conducted by Parmar

and Khodasevych [31], [39].

There are several design criteria required for a successful interfacial pressure sensor. First, the

sensor must be constructed on a thin, flexible substrate. A rigid substrate would create stress-

concentrations at sensor edges, especially at anatomically curved areas, creating tension in the

skin and altering the pressure distribution [41]. The sensor should be able to be easily integrated

9

within the prosthetic socket or other MAT interface, to ensure consistent measurements are

obtained. An accuracy and repeatability of 90% is acceptable in clinical applications [31]. Drift,

or gradual change in output independent of applied pressure, should be quantified to enable

compensation in the calibration to maintain 90% accuracy.

The Peratech QTC™ sensor is a pressure sensitive membrane switch. Carbon finger electrodes

are utilized, extending into a silver electrical connections. A patch of PET printed with carbon

and overprinted with quantum tunneling composite (QTC) ink is assembled over finger

electrodes, separated by an adhesive spacer outlining the active sensing area [42]. The quantum

tunneling composite consists of nickel nanoparticles immersed within an elastomeric polymer

matrix. At rest, the polymer acts as an insulator as the metal particles are not in contact with each

other. Upon deformation, the nanoparticles approach each other, causing the electrical resistance

to drop by several orders of magnitude [43]. Quantum tunneling is achieved by precisely

controlling the shape and blending process used with the nanoparticles. Peratech Ltd. has

commercialized several thin film QTC pressure sensors fabricated using conventional methods

(i.e. not AM).

The Sensitronics ThruMode™ FSR is constructed by affixing two identical layers together

separated by a spacer, where each of two layers is constructed by depositing a solid

semiconductive force-sensing resistor (FSR) element on top of a screen printed solid conductive

silver pad [10]. The FSR element consists of a conductive polymer that operates on the principles

of quantum tunneling, as described previously. The spacer creates a gap when the sensor is

unloaded, and a conductive path between layers when loaded When subjected to a load, the

resistance change is foreseeable (an approximately linear relationship between resistance and

magnitude of the applied load).

The FlexiForce® sensor by Tekscan operates on the same principles as the Sensitronics

ThruMode™ FSR, and is constructed of two layers of polyester substrate that each have a

conductive silver layer, overprinted with a layer of pressure-sensitive ink [44].

10

2.1.2 Sensor Evaluation Studies

Various sensor evaluation studies have been performed; however, the studies have significant

limitations. A sensor evaluation study performed by Ferguson-Pell et al. evaluated the

FlexiForce® for low interface pressure applications. This study included static testing, where

loads were applied to the sensors for periods from 1 min to 2 hours using a low friction tilt table

[45]. The pressure range under study was from 0 to 50 mmHg (0 to 6.7 kPa), targeting interface

pressures reported for residual limb bandages and pressure garments [45]. The researchers

concluded the sensor had acceptable drift, repeatability, linearity, and hysteresis; however,

curvature was identified as a limitation of the sensor. The authors identified future study is

needed on sensor performance when the entire sensing area is not loaded [45].

A study performed by Likitlersuang et al. studied the effects of calibration and sensor-surface

condition (i.e., three conditions: no load disc, rigid disc between sensor and skin, and disc

between sensor and load applicator) on FlexiForce® sensors [9]. The findings revealed that

properly calibrating the sensor with conditions mimicking the test conditions, and placement of a

disc between the sensor and the human body can drastically reduce measurement errors, from

23.3 ± 17.6% to 2.9 ± 2.0 %. The researchers recommended the effects of the area of the applied

load be examined in future work [9].

Parmar et al. published a study in 2017 evaluating the performance of 5 low-cost commercially

available sensors on a human-leg-like test apparatus [31]. The applied pressure range was

between 2.7- 12 kPa, simulating compression therapy applications. The authors used a dead-

weight apparatus and applied simplified static and dynamic loading profiles to the sensors. The

results displayed the Peratech QTC™ was the only sensor that achieved accuracy values of 90%

or higher in both static and dynamic tests. Khodasevych et al. published a study using a similar

apparatus and loading profile to Parmar et al., evaluating the effects of curvature on sensor

performance. Again, the authors found the Peratech QTC™ sensor to be the top-performing

sensor [39].

A study performed by Jensen et al. acknowledged that commercial FSR performance is

dependent on area of applied load. To ensure the pressure sensors studied measured force,

independent of area, an epoxy dome was placed on top of the sensing area of the sensor. Figure

2-4 displays the apparatus used. This prototype was designed for measuring external forces from

11

fingertips (e.g., pinching and grasping tasks), therefore it is not ideal for monitoring the interface

of a larger area as the dome tip can interfere with surface pressures.

Figure 2-4: Experimental apparatus used by Jensen et al [46].

Previous sensor evaluation studies have used simplified dynamic loading patterns [9], [31], [39].

For example, the dynamic loading profiles in studies by Parmar et al. and Khodasevych et al.

were defined as 10 cycles of the application of a load for 30 seconds on and 30 seconds off [31],

[39]. This loading pattern was meant to simulate dynamic wear of a prosthesis; however, it is an

overly simplistic model. Most sensor evaluation studies focus on static testing, in which dead-

weights are loaded onto pressure sensors for time periods of 2.5 minutes [9]; 20 minutes,

representing the time period for adjustments and alternations during a clinical visit [35]; 2 hours

[45]; or 8 hours, simulating full-day wear [31], [39]. Thus, there is a need for a dynamic sensor

evaluation protocol simulating MAT wear and use.

2.1.3 Sensing Configurations

Several different factors and configurations affect FSR performance. Loading pucks, as

recommended by manufacturers, ensure the force applied to the sensor is transmitted fully

through the sensing area [10], [44]. Figure 2-5 displays how the effective loading area changes

with the presence of an elastomer puck when the area of applied load is larger than the sensing

area.

12

Figure 2-5: Loading configuration with and without an elastomer puck highlighting the difference

in effective loading area when the area of applied load is larger than the FSR’s sensing area.

Sensor manufacturers also recommend the sensors be mounted on “relatively rigid” surfaces to

prevent sensor bending and improve uniformity of force distribution across the sensor [10], [44].

However, this is not representative of human tissue, and the presence of a rigid surface has the

potential to alter stress distributions at the interface and cause pain to the user [41].

2.2 Pressure Distributions at the Body-Device Interface

2.2.1 Pressure Distributions During Gait

Typical walking gait consists of two phases: stance-phase and swing-phase. Stance phase is

initiated by heel-strike and refers to the period while the foot is in contact with the ground.

Swing phase, initiated by toe off, refers to the period when the foot is not in contact with the

ground and is propelling forward. One full gait cycle is initiated by the heel strike of one foot

and continues until the same foot prepares to strike for the next step. A single gait cycle is also

known as a stride and is displayed in Figure 2-6.

13

Figure 2-6: Typical walking gait phases [47]. Stance and swing phases are labelled for one gait cycle

on the highlighted (right) leg.

The pressure and force distributions and magnitudes vary at the MAT body-device interface

throughout gait. Figure 2-7 displays the typical interface pressure profile at a residual limb-

socket interface of a transtibial amputee during one stride, from a study performed by Dumbleton

et al. [48]. The points displayed on the plot represent the following: 1=heel contact, 2=peak early

stance, 3=low mid stance, 4=peak late stance, 5=toe-off.

Figure 2-7: Typical interface pressure profile at the residual limb-socket interface of a

transtibial amputee during one stride [48].

A pressure range of 5 to 150 kPa represents conditions applied to the residual limb of lower-limb

amputees during gait [8]. This range encompasses conditions applied to the body under many

other MAT applications, including compressive garments with a range of 5 to 12 kPa [31], and

ankle-foot orthotics with a range of 15 to 130 kPa [33].

14

Both force and pressure measurements are used to describe interfacial distributions in the

literature. Pressure, which is calculated by dividing the applied force by the applied area,

provides insight to the concentration of the force on the tissue, which is typically more indicative

of the pain levels experienced by users than simply absolute force [49]. The range of 5 to 150

kPa can then be translated to forces of 0.4 to 11.8 N given a circular area with a diameter of 10

mm (i.e., sensing diameter of common commercial sensors). In this study, applied loads were

presented with force units (i.e., N). This selection ensured the force remained constant as the area

of applied load was subject to change.

2.2.2 Normal and Shear Loading

Both normal and shear stresses are present at the body-device interface in many MAT

applications. The magnitudes of normal stresses are typically higher than shear stresses, with

reported peak pressures at of a transtibial (below-knee) prosthetic socket site being reported at

roughly 90 kPa for normal stresses and below 10 kPa for shear stresses [50]. The ratio of shear

stress to pressure in a trans-tibial socket interface varies according to the progression of stance,

as well as the location of measurement [51]. Several studies have been performed to determine

the effects of both shear and normal forces on tissue [3], [36], [52]. It was concluded that both

shear and normal stresses have roughly the same effects on tissue, and it is important to consider

the resultant, or combined, stress vector [52].

As the QTC prototype sensor is under development, the initial prototype will target uni-

directional sensing. Thus, as the magnitudes of normal stresses are larger than shear stresses, the

sensor will be initially designed to monitor normal stresses.

2.3 Additive Manufacturing

2.3.1 3D-Printed Assistive Devices

Additive manufacturing has been used in many MAT applications, including prosthetics and

orthotics [14]. In the design stage, a 3D-printed below-knee prosthetic socket with variable

compliance has demonstrated a reduction in contact pressure [16]. Nia Technologies, a Canadian

non-profit social enterprise, is developing 3D-printed prosthetics and orthotics for pediatrics in

developing countries [53]. They are in the process of improving their device design, and have

completed clinical trials in Cambodia, Tanzania, and Uganda. Commercial applications of AM in

15

MAT include the “Cyborg Beast,” a low-cost prosthetic hand for children fabricated using AM

methods [54]. Current limitations to implementation of AM for MAT devices include inadequate

material strength and high initial investment costs [14]. Once these limitations are resolved, full

clinical implementation of AM for custom prosthetics and orthotics is anticipated [14].

2.3.2 3D-Printed Pressure Sensors

A recent industry shift towards AM has led to significant advances in 3D-printed sensing

technology [18]. The need exists for a sensor that can be 3D-printed and easily integrated into

MAT devices. Several researchers have developed pressure sensors in which the sensing element

itself, or the sensor frame is 3D-printed. Laszczak et al. developed a capacitance-based sensor

with a 3D-printed frame for monitoring interface pressure in lower-limb amputees [15]. The

authors identified evaluation of sensor compliance with curved surfaces, sensor flexibility, and

formation of sensor matrices as directions for future work. Lin and coworkers designed an

optical pressure -sensing element in a 3D-printed body, and Saari and coworkers designed a 3D-

printed capacitive force sensor consisting of a frame with embedded wires and a thermoplastic

elastomer [55], [56]. These sensors and others have shown promising initial results, however

further sensor development and evaluation is required for clinical use [12], [56], [57].

Until recently, elastomers could not be 3D-printed due to their material properties. Unlike

thermoplastics often used in AM, elastomers cannot be heated and printed into specific shapes.

Recent advances in printing technology have enabled the 3D-printing of silicone and silicone-

nickel composites [58]. These developments will enable the 3D-printing of quantum tunneling

composite sensors. Previous studies have demonstrated QTC sensors have excellent potential for

pressure sensing applications at the body-device interface [31], [39]. Elastomers will provide the

flexible frame required for interfacial pressure sensors, enabling integration at the body-device

interface.

16

Chapter 3

Commercial Sensor Evaluation

3.1 Load Area and Sensor Configuration

This section contains segments of a manuscript entitled “Evaluating the Effects of Load Area and Sensor

Configuration on the Performance of Force Sensors at Simulated Body-Device Interfaces” authored by

Megan Hamilton, Kamran Behdinan, and Jan Andrysek. This manuscript is currently under review for

publication. The limitations and future work are summarized in Chapter 5 to improve the flow of

information and reduce repetition.

3.1.1 Introduction

Force and pressure sensors can be used at the body-device interface to assess the fit of assistive

devices; provide input for control in robotics; and inform designers, researchers, and clinicians

monitoring interfacial pressure distributions. Single-point thin-film force-sensitive resistors

(FSRs) are often used for interface pressure measurement due to their thin, flexible construction,

adequate sensitivity, and ease of use [31]. There are several commercially available single point

FSRs, including the QTC™ sensor (Peratech Ltd, Richmond, North Yorkshire, UK); the

Sensitronics ThruMode™ FSR (Sensitronics, Bow, WA, USA); and the FlexiForce® sensor

(Tekscan, Boston, MA, USA). Such sensors have been used in a variety of biomedical

applications, however, there is limited data regarding sensor performance at the body-device

interface [8], [9], [31], [39], [59], [60]. Applications include, but are not limited to, prosthetic

and assistive devices [12], [28], laparoscopic instruments [29], compression garments [31], in-

shoe gait analysis devices [33], and robotics [34].

The force-resistance relationship of an FSR depends on sensor shape, geometry, and ink

formulation, as well as actuator geometry and rigidity [10]. Commercial sensor manufacturers’

guidelines state the area of applied load should be held constant at an area slightly smaller than

the sensors’ sensing areas [10], [44], [61]. However, this is not representative of conditions at the

body-device interface, where the loading area fluctuates and typically is larger than the sensing

area. When the area of applied is load is larger than the sensor, manufacturers recommend a thin

piece of silicone rubber 20% smaller than the sensing diameter, also known as a load “puck”, be

placed between the sensor and load applicator to ensure the force is distributed evenly over the

17

sensing area [10]. Sensor manufacturers also recommend the sensors be mounted on “relatively

rigid” surfaces to prevent sensor bending and improve uniformity of force distribution across the

sensor. However, this is not representative of the human tissue, and the presence of a rigid

surface has the potential to alter stress distributions at the interface and cause pain to the user

[41].

A limited number of studies have evaluated sensor performance under conditions that simulate

the body-device interface. A study performed by Likitlersuang et al. studied the effects of

calibration and sensor-surface condition (i.e., three conditions: no load disc, rigid disc between

sensor and skin, and disc between sensor and load applicator) on FlexiForce® sensors [9]. The

findings revealed that properly calibrating the sensor with conditions mimicking the test

conditions, and placement of a thin rigid disc between the sensor and the human body can

drastically reduce measurement errors, from 23.3 ± 17.6% to 2.9 ± 2.0 %. The researchers

recommended the effects of the area of the applied load be examined in future work [9]. Parmar

et al. evaluated the performance of five low-cost commercially available sensors on a test

apparatus simulating the human-leg using deadweights for static and simplified dynamic loading

[31]. The Peratech QTC™ and Sensitronics FSR were found to be the top performing sensors

when drift, accuracy, and repeatability were evaluated. Under static testing conditions, the

Peratech sensor realized an average accuracy in pressure measurements ranging from 83.7 to

98.1%, depending on the force level, while the Sensitronics sensor realized an accuracy ranging

from 84.3 to 87.2% [31]. A similar study by Khodasevych et al. then used the same apparatus

and evaluated the effects of curvature on the Peratech QTC™ and Sensitronics FSR. Their main

finding was that matching calibration conditions (i.e., curvature and compliance) to testing

conditions drastically improved accuracy [39]. Neither of these, nor any other studies, were

found to have examined the effects of area of applied load.

The overall objective of this study was to evaluate the performance of commercially available

thin-film FSRs under conditions simulating the body-device interface. Specifically, the objective

was to determine the effects of load area and sensor configuration (i.e., load puck and rigid

backing) on two commercially available thin-film FSRs under conditions simulating the body-

device interface. A sub-objective of the study was to understand the effects of two calibration

techniques on sensor performance, including calibrating under exact and approximated

conditions.

18

3.1.2 Methods

Testing was conducted to determine the effect of area of applied load on sensor accuracy and

repeatability through various loading puck configurations. Loading configurations following

sensor manufacturer recommendations and simulating more clinically-relevant conditions were

used.

3.1.2.1 Sensors

Two commercially available sensor models, the QTC™ SP200-10 sensor (Peratech Ltd,

Richmond, North Yorkshire, UK) and the ThruMode™ FSR (Sensitronics, Bow, WA, USA),

were selected due to their top performance in similar studies evaluating the performance of

sensors at body-device interfaces [31], [39]. Images of both sensors are displayed in Figure 3-1.

Sensor model specifications are outlined in Table 3-1. Both sensor manufacturers note the

reported part-to-part repeatability is dependent upon consistent actuation.

Figure 3-1: Peratech (top) and Sensitronics (bottom) sensors.

Table 3-1: Commercial sensor model specifications.

Parameter QTC™ SP200-10 Half Inch ThruMode™ FSR

Manufacturer Peratech Ltd. Sensitronics Inc.

Sensing Diameter (mm) 10 12.7

Thickness (mm) 0.45 0.43

Claimed Operating Range (N) 0.1 to 20 0.26 to 26a

Single Part Repeatability (%) N/Ab 5

Part-to-Part Repeatability (%) 4.5 15

a Reported as 0.3 – 30 psi, converted to N using sensing area. b Not reported.

19

3.1.2.2 Data Acquisition System

The Instron Bluehill Universal software (Instron, Norwood, MA, USA) was used for collection

of time and force data. The resistance values (output) of the pressure sensors were measured

using a Keithley 2100 6 ½ Digit Multimeter (DMM) (Tektronix, Inc., Beaverton, OR, USA),

collected using the KI-LINK Excel® Add-In (Tektronix, Inc., Beaverton, OR, USA) and

analyzed using MATLAB (The MathWorks, Inc., Natick, MA, USA).

3.1.2.3 Testing Apparatus

A testing apparatus was developed to simulate human soft-tissue at a body-device interface. A

photo of the actual setup and a labelled schematic showing sensor configuration are displayed in

Figure 3-2. A flat 3D-printed base secured to the Instron base platen via interference fit (PLA

White Material; Ultimaker 2 Printer; Ultimaker B.V., Netherlands) was covered with a 2 cm

outer layer of soft translucent silicone (Renew ® Silicone 10, Renew®, Easton, PA, USA). The

selected silicone type has been proven to mimic the damping and load-dispersal behavior of

human soft-tissue [6], [62], [63]. Loads were applied using an Instron 5944 Universal Testing

System with a 100 N load cell (Instron, Norwood, MA, USA). A force range from 0 to 10 N was

selected [64], [65], to be within both sensors’ working range.

Figure 3-2: Actual setup (left) and labelled schematic (right) showing configuration with both

loading puck and rigid backing.

Loading tip attachments were 3D-printed (PLA, printing process same as described above) and

secured to the upper compression platen via interference fit with the following contact area

diameters: 5 mm, 8 mm, 15 mm, and 25 mm. The smallest 5 mm tip was selected to display

20

effects when only a section of the sensing area is loaded. The 8 mm tip was designed to follow

manufacturer’s recommendations, with the diameter of the load applicator being 20% smaller

than the smallest sensor’s sensing diameter (Peratech) to ensure the spacer and adhesive do not

interfere with the applied force [10]. Larger areas simulate clinically relevant conditions where

the area of applied load is larger than the sensor’s sensing area.

To determine the effects of sensor configuration, a loading puck and rigid backing were used as

per sensor manufacturers’ recommendations. A 1.5 mm thick silicone loading puck (Durometer

60 Shore A) with diameter 8 mm was used to ensure all the force was transmitted through the

sensing area. A 0.6 mm thick polyoxymethylene disc with diameter 14.5 mm was used as a rigid

backing to prevent sensor bending.

3.1.2.4 Protocol

3.1.2.4.1 Application Conditions

To determine the effects of using an elastomer loading puck and rigid sensor backing, a full

factorial experiment was performed on both sensor models using a total of 16 application

conditions: each of four loading tip areas under four loading configurations. The loading

configurations are as follows: i) rigid backing with elastomer puck (as seen in Figure 3-2), ii)

rigid backing with no elastomer puck, iii) no rigid backing with elastomer puck, and iv) no rigid

backing or elastomer puck. Table 3-2 displays the 16 application conditions.

Table 3-2: Application conditions: Combinations of variables tested

Application

Condition

Area

(Diameter (mm)) Backing Puck

1 5 NB NP

2 5 NB YP

3 5 YB NP

4 5 YB YP

5 8 NB NP

6 8 NB YP

7 8 YB NP

8 8 YB YP

9 15 NB NP

10 15 NB YP

11 15 YB NP

12 15 YB YP

13 25 NB NP

14 25 NB YP

15 25 YB NP

16 25 YB YP

21

3.1.2.4.2 Sensor Conditioning and Testing

Prior to each experiment, the sensor was conditioned as per commercial manufacturer guidelines

[44]. This involved applying 110% of the maximum test load, or 11 N, to the sensor for 30

seconds, repeated four times.

For each of the 16 application conditions, forces of 2, 4, 6, 8 and 10 N were applied to the sensor

being tested. The order of applied force and application condition was randomized to account for

potential testing bias. Each force was applied to the sensor for 15 seconds, and force and

resistance values were collected at 2 Hz. The load was then removed for 10 seconds between

forces to reset any drift effects. For each force level under the 16 application conditions, the

average resistance was calculated over the final 7.5 seconds of loading to allow the sensor output

to stabilize. The force and average resistance values then provided the basis for the force-

resistance relationship and enabled the calculation of a resistance vs. force curve for the sensor.

These equations were curve fit using a power relationship as per manufacturer guidelines [10],

which represents the characteristic curve for the application condition and can be used to convert

resistance to force. The entire testing protocol was repeated twice on two different sensors per

sensor model (i.e., two different Peratech sensors and two different Sensitronics sensors), for a

total of four replicates per sensor model.

3.1.2.4.3 Analysis

Sensor performance was assessed using the coefficient of variation (CV). A repeatable FSR

output is required to obtain accurate results. The coefficient of variation (CV) is a measure of

sensor repeatability defined as the ratio of the standard deviation (SD) to the mean. It has been

used to evaluate sensor performance [9], [45]. The Sensitronics manufacturer reports a part-to-

part repeatability of 15% and a single part repeatability of 5%, while the Peratech manufacturer

reports only the part-to-part repeatability (CV) of 4.5% [42], [66]. In general, a CV of 10% is

acceptable in clinical applications [31].

An analysis of variance (ANOVA) was used to examine the effects of the various factors

(diameter, backing, puck, and force) on the CV data for each sensor model (i.e., Peratech and

Sensitronics). All main effects, 2-way and 3-way interactions were evaluated with p<0.05

indicating significance. Insignificant effects were then removed from the model, and significant

22

main effects, 2 and 3-way interactions were reported. JMP® Pro 14 software was used for the

statistical analysis (SAS Institute Inc., Cary, NC, USA).

As part of our sub-objective two different calibration methods were used in this study to

understand the effects of matching the calibration settings to the experimental settings. One

method, the matched-area (MA) calibration, is a more accurate, time-consuming method in

which the exact configuration used during testing is matched during the calibration. The second

method, generalized-area (GA) calibration, is a time-saving approach where one configuration

used during calibration is then applied to multiple configurations during the experimental testing.

For the matched-area calibration, the CV represented the variation in average resistance across

the 4 trials for each configuration, calculated at all 5 force levels and each of 16 application

conditions. For the generalized-area calibration, the CV represented the variation in average

resistance across 4 trials for the given area of applied load compared to the standard 8 mm

applicator tip, calculated at all 5 force levels and each of 16 application conditions. The CV will

be the same for both MA and GA calibrations for the 8 mm conditions. An overall across-

configuration CV method was also calculated, representing the variation across all trials (i.e., all

areas, force levels, and replicates) for a given configuration (i.e., NBNP, NBYP, YBNP, YBYP).

While sensor manufacturers recommend calibration conditions that mimic sensor use, this is not

always possible in clinical situations at the body-device interface (i.e., inconsistent actuation and

area of applied load); hence the relevance of the simplified generalized-area calibration

technique. A pair-wise t-test was performed on each sensor model comparing the MA and GA

calibrations.

3.1.3 Results

The force-resistance data and curve fitting results show the characteristic force-resistance curve

for the four replicates of all 16 application conditions, displayed in Figure 3-3 and Figure 3-4 for

the Peratech and Sensitronics sensors, respectively. Subplots are grouped by configuration: i) no

rigid backing or elastomer puck (NBNP), ii) no rigid backing with elastomer puck (NBYP), iii)

rigid backing with no elastomer puck (YBNP), and iv) rigid backing with elastomer puck

(YBYP). Line colours differentiate the area of applied load, as displayed in the legend. Line

styles (i.e., solid, dashed, dotted and dashed-dotted) indicate the four replicates per sensor. No

23

significant differences were observed between the two sensors tested for each sensor model, so

the sensor number variable was not subjected to further analysis.

Figure 3-3: Characteristic resistance vs. force curve for the Peratech sensor under all 16 application

conditions.

Figure 3-4: Characteristic resistance vs. force curve for the Sensitronics sensor under all 16

application conditions.

24

Matched-area calibration CV is visually represented by the variance in lines of a given colour on

each subplot in Figure 3-3 and Figure 3-4 (i.e., variance across replicates given identical

conditions). Generalized-area calibration CV is visually represented by the variance in all four

lines of a given colour (any area) compared to the black lines (standard 8 mm diameter) on each

subplot. Overall, the Peratech sensor displays increased repeatability compared to the

Sensitronics sensor under the application conditions. For both sensors, the presence of a load

puck appears to greatly improve repeatability, which can be observed in the convergence of the

right-most subplots (with loading puck) for both Figure 3-3 and Figure 3-4, compared to the left

two subplots (without loading puck). The greatest variance is observed in the no-puck conditions

with areas larger than the sensing area (15 and 25 mm diameter).

Table 3-3 summarizes the CV for both Peratech and Sensitronics sensors, using both MA and

GA calibration methods. CV values are displayed for each configuration, averaged over the five

force levels. The error is the standard deviation in CV across force levels. A red-yellow-green

colour gradient was applied to the cells indicating where each CV falls within the entire table

range. The findings from the characteristic curves in Fig. 3 and 4 described above are confirmed

in this table. The Peratech sensor generally outperforms the Sensitronics sensor and the no-puck

and large area conditions display the greatest variance. However, this difference is not

statistically significant, due to the outliers in the Peratech data (i.e., large CV for 15YBNP

configuration). A significant difference between MA and GA calibration methods was reported

only for the Sensitronics sensor. Furthermore, the generalized-area calibration method is best

under conditions when the area of applied load is smaller than the sensor (5 and 8 mm), or in the

presence of a load puck.

25

Table 3-3: Coefficient of variation values by area and configuration. Configuration Coefficient of Variation (%)

Tip

(mm)

Config

Peratech

MA

Peratech

GA

Peratech

Across

Config

Sensitronics

MA

Sensitronics

* GA

Sensitronics

Across

Config

5

NBNP

5.1 ± 1.5 8.3 ± 0.9

44.8 ± 16.5

7.6 ± 3.1 16.7 ± 5.1

108.5 ± 7.6 8 3.1 ± 1.8 3.1 ± 1.8 13.9 ± 6.7 13.9 ± 6.7

15 7.2 ± 4.1 34.0 ± 19.6 48.7 ± 8.0 109.9 ± 6.7

25 36.7 ± 6.5 52.8 ± 13.5 23.1 ± 12.3 92.1 ± 10.3

5

NBYP

5.0 ± 2.3 7.8 ± 1.6

7.7 ± 1.1

11.5 ± 2.7 15.2 ± 3.5

16.4 ± 4.5 8 7.9 ± 2.0 7.9 ± 2.0 15.9 ± 1.2 15.9 ± 1.2

15 6.4 ± 1.4 7.3 ± 1.7 17.3 ± 1.2 16.7 ± 0.9

25 6.4 ± 0.9 7.3 ± 1.5 12.9 ± 5.7 15.4 ± 3.4

5

YBNP

6.9 ± 1.5 16.1 ± 4.8

69.9 ± 37.0

14.6 ± 7.2 12.9 ± 5.0

199.3 ± 51.9 8 16.5 ± 9.1 16.5 ± 9.1 9.1 ± 3.1 9.1 ± 3.1

15 28.1 ± 10.0 56.4 ± 20.8 98.9 ± 35.7 172.7 ± 41.0

25 36.5 ± 46.0 50.0 ± 72.2 113.8 ± 19.2 187.5 ± 23.0

5

YBYP

7.3 ± 1.2 9.8 ± 0.7

9.4 ± 0.7

15.5 ± 2.8 20.5 ± 2.9

21.5 ± 5.0 8 5.2 ± 0.5 5.2 ± 0.5 17.4 ± 5.7 17.4 ± 5.7

15 4.8 ± 0.6 5.4 ± 0.4 18.7 ± 8.5 19.5 ± 4.4

25 4.2 ± 0.5 5.7 ± 1.7 15.1 ± 3.2 18.8 ± 2.7 * Indicates Sensitronics generalized-area calibration is significantly different from matched-area calibration (p

< 0.05) based on pair-wise t-test.

Coefficient of variation values averaged over the five force levels. The error is the standard deviation in CV

across the force levels.

Table 3-4: ANOVA results (p-values) from generalized-area calibration data.

Effect Peratech Sensitronics

Puck <0.0001* <0.0001*

Area <0.0001* <0.0001*

Backing <0.0001* <0.0001*

Force 0.00938* 0.774

Area*Puck <0.0001* <0.0001*

Backing*Puck <0.0001* <0.0001*

Puck*Force 0.03977* 0.872

Area*Backing <0.0001* 0.00113*

Area*Force 0.268 0.960

Backing*Force 0.232 0.26

Area*Backing*Puck <0.0001* 0.00113*

Area*Backing*Force 0.448 0.695

Area*Puck*Force 0.203 0.660

Backing*Puck*Force 0.126 0.410

* Indicates statistical significance (p < 0.05).

Table 3-4 displays the p-values (ANOVA results) calculated for the main effects and two and

three-way interactions. Figure 3-5 and Figure 3-6 show the two-way interaction profiles for the

Peratech and Sensitronics sensors, respectively. Each cell contains line segments plotted for the

26

interaction of the row effect with the column effect. Response values predicted by the model are

connected by line segments. Non-parallel line segments indicate potential interactions, and

significant interactions are confirmed with the p-values in Table 3-4.

Figure 3-5: Interaction profiles for Peratech sensor using matched-area calibration data.

Figure 3-6: Interaction profiles for Sensitronics using matched-area calibration data.

The presence of a 3-way interaction indicates that the lower-order interactions including area,

backing, and puck are conditional on the 3-way interaction. One significant 3-way interaction

27

was observed for both sensors (Area*Backing*Puck). The combination of absent puck and

present backing significantly reduces repeatability at larger areas (diameters of 15 and 25 mm).

Under the no-puck conditions, the 25 mm attachment has better repeatability than the 15 mm

attachment. This can be seen in the Area*Backing and Area*Puck interaction profiles for both

sensors. Repeatability is significantly different only at the larger load applicators (15 and 25

mm), where the presence of a backing increases the CV, and therefore decreases repeatability,

and the presence of the puck improves the repeatability. Significant interactions are also

observed between backing and puck, where the presence of the backing improves the CV only

with the presence of the puck as well.

The presence of a puck had the strongest effect on repeatability for both sensors, with a p-value

of <0.0001. Area was significant for both sensors, with the smaller load applicators resulting in

significantly lower CV results. For both sensors, the backing effect was significant, with no

backing resulting in more repeatable results. Force is a significant factor for the Peratech sensor

(p<0.05), but not the Sensitronics sensor (p=0.77). For the Peratech sensor, the repeatability is

significantly increased for the 10 N force (p=0.02), but significantly lower for the 2 N force

(p<0.05).

3.1.4 Discussion and Conclusions

FSRs are often used for interface pressure measurement in biomedical applications due to their

thin, flexible structure, adequate sensitivity, and ease of use. Manufacturers recommend use on

rigid surfaces with consistent actuation to ensure accurate results [10]. However, the body-device

interface features compliant surfaces and dynamic, inconsistent actuation. This study explored

the effects of area of applied load and sensor configuration (elastomer loading puck and thin

rigid backing disc) on the repeatability of two common commercially available FSRs, the

Peratech and the Sensitronics sensors, under conditions simulating the body-device interface.

The work also examined the effects of matching the calibration settings to the experimental

settings.

Overall, the area of applied load and sensor configuration had significant effects on sensor

repeatability. It should be noted that because the area, puck, and backing factors are involved in a

3-way interaction, the main effects of each are also dependent upon the other factors.

28

The presence of a compliant puck had the strongest effect on repeatability for both sensors, with

a clear indication that the puck improves repeatability. The area also had a significant effect,

where in general, the areas larger than the sensing area (15 and 25 mm diameters) resulted in

greater variance. The presence of a backing also resulted in greater variance overall.

Without the presence of a loading puck, the load tips that are larger than the sensor’s sensing

area apply the load inconsistently. The load is transmitted through the surrounding silicone, the

sensor’s adhesive non-sensing edge, and the sensing area. Therefore, the sensor output will

greatly vary when subjected to the same load, depending how the load is being transmitted. This

effect is most significant for the larger areas without loading pucks.

Under the no-puck conditions, the 25 mm attachment has better repeatability than the 15 mm

attachment. Since the area of the 25 mm attachment is significantly larger than the sensor’s area,

when the load is applied the silicone layer around the sensor is displaced downwards, resulting in

load transmission through the sensing area. The 15 mm attachment is only slightly larger than the

sensor’s total area and is more likely to transfer the load directly through the sensor’s adhesive

layer, not the surrounding silicone. The effect of the backing, in combination with the absence of

a puck and the larger area, reduces sensor bending such that the load is then transmitted through

the sensor’s adhesive layer, not the sensing area. Conversely, for the smaller load applicators (5

and 8 mm), the load is entirely transmitted through the sensing area, regardless of backing or

puck presence.

Force is significant as a 2-way interaction with the use of the puck for the Peratech sensor only.

Thus, the Peratech sensor is more sensitive to the force range, with higher repeatability at higher

forces, compared to the Sensitronics sensor’s performance that is more consistent across the

force range.

In true clinical applications, the body-device interface will apply conditions where the area of

applied load is larger than the sensing area. It is also important to note that this area will also

likely be changing dynamically. To enable repeatable results, the use of an elastomer load puck

that transmits the load through the sensing area is recommended. The presence of a rigid backing

is not recommended based on our results, as it may decrease repeatability. Additionally, the

presence of the rigid layer, which is recommended by manufacturers [10], can alter the stress

distributions at the body-device interface.

29

Significant differences were observed between the matched-area and generalized-area calibration

methods for the Sensitronics sensor only. The Peratech sensor is thus less sensitive to effects of

area of applied load. Without a loading puck, these effects would need to be addressed.

However, the difference in CV between the two calibration methods is decreased with the

presence of a loading puck. This is because the puck transmits the load through the puck area,

ensuring all the load is received by the sensing area. The use of a loading puck thus justifies the

generalized-area calibration technique. Furthermore, the generalized-area calibration method is

best under conditions when the area of applied load is smaller than the sensor (diameters of 5 and

8 mm), or in the presence of a load puck.

Generally, the Peratech sensor provided more repeatable measurements than the Sensitronics

sensor under the various configurations. With the optimal configuration (NBYP – without

backing and with puck), the Peratech CV ranged from 5.0 ± 2.3 to 7.9 ± 2.0 % for the matched-

area calibration method, and 7.3 ± 1.5 to 7.8 ± 1.6 % for the generalized-area calibration

methods. Both ranges are within the acceptable threshold of 10% for clinical use [31]. Under the

same configuration, the Sensitronics CV ranged from 11.5 ± 2.7 to 17.3 ± 1.2 % for the matched-

area, and 15.2 ±3.5 to 16.7 ±0.9 % for the generalized-area calibration, both above the 10%

threshold. Overall, the presence of an elastomer load puck can improve repeatability across all

configuration conditions and enable the use of a generalized-area, time-saving calibration

method.

It is worth noting that the CV values reported in this study agree with the accuracy values

reported in the study by Parmar et al [31]. Parmar et. al reported average accuracy in pressure

measurements ranging from 83.7 to 98.1% (equivalent to a CV of 1.9 – 16.3%) for the Peratech

sensor and 84.3 to 87.2% (equivalent to a CV of 12.8 to 15.7%) for the Sensitronics sensor [31].

These values are also relatively close to the manufacturer reported part-to-part repeatability of

5% for the Peratech sensor and 15% for the Sensitronics sensor. While the findings coincide well

with these previous reports, they provide important new information about the effects of

application-relevant factors such as area and calibration and their interactions, which have not

been previously explored. Limitations to the study are included in Section 5.1.

30

3.2 Tissue Compliance

Evaluating the Effects of Tissue Compliance, Load Area and Sensor Configuration on the

Performance of Pressure Sensors at Simulated Body-Device Interfaces

3.2.1 Introduction

The properties of the tissue in contact with an assistive device are important to consider.

Surrounding tissues are responsible for significant load-bearing and softer tissues promote

uniformity of the pressure distribution, lowering peak interface stresses [67]. Tissue compliance,

or stiffness, has been identified by researchers as a parameter that should be studied to evaluate

interfacial pressure sensor performance at the body-device interface [39]. Limited studies have

explored the effects of material compliance on interfacial sensor performance, however the

studies that have been conducted have significant limitations. A study by Khodasevych et al.

evaluated two different materials: soft (i.e. silicone) and rigid (i.e. aluminum) [39]. There is thus

a need to evaluate the effects of a range of soft materials, representative of tissue.

Other studies have simulated soft tissue for a range of biomedical applications. Examples include

a 5 mm thick silicone elastomer [68], a 20 mm thick polymer [69], and a 20 mm thick silicone

elastomer [31], [39]. The apparent hardness of a material is a function of sample thickness and

the product grade (i.e. manufacturer stated hardness). Apparent or measured hardness increases

inversely with thickness, therefore thicker samples will appear more soft than thinner samples of

the same material [70]. To understand the effects of stiffness on sensor performance, three

different soft materials were evaluated in this section.

Section 3.1 Load Area and Sensor Configuration above included the evaluation of the effects of

load area and sensor configuration (i.e., presence of a rigid backing and load puck) on the

performance of two commercially available sensors. In this section, an additional factor, tissue

compliance, was included in the evaluation. Three different silicone samples of varying hardness

were evaluated.

Specifically, the objective of this section was to determine the effects of tissue compliance, load

area, and sensor configuration (i.e., load puck and rigid backing) on two commercially available

thin-film FSRs under conditions simulating the body-device interface. The effects of two

calibration techniques on sensor performance will be evaluated as a sub-objective.

31

3.2.2 Methods

The sensors and testing apparatus used in Section 3.1 Load Area and Sensor Configuration were

repeated for the testing in this section. The data acquisition system was modified slightly, using a

a Keithley 6500 6 ½ Digit Multimeter (DMM) (Tektronix, Inc., Beaverton, OR, USA) and

Tektronix’s proprietary KickStart software (Tektronix, Inc., Beaverton, OR, USA). Time and

force data were collected using the Instron Bluehill Universal software. All data was collected at

500 Hz. Resistance, force, and time data were analyzed using MATLAB v19 (The MathWorks,

Inc., Natick, MA, USA).


The testing apparatus used in Section 3.1 Load Area and Sensor Configuration was repeated for

this section. However, instead of using only one material beneath the sensor simulating tissue,

three different materials were used. The original sample, referred to as the “Extra Soft” material

was selected in addition to two firmer materials. The three different materials evaluated are

described in Table 3-5.

Table 3-5: Specifications for three compliant materials tested.

Identifier Thickness Shore Hardness Product Grade Material Manufacturer

Extra Soft 20 mm 10A Translucent Silicone Renew®

Soft 10A 6.5 mm 10A Silicone McMaster-Carr

Soft 20A 6.5 mm 20A Silicone McMaster-Carr

3.2.2.2 Protocol

3.2.2.2.1 Application Conditions

A total of 48 application conditions were tested. Each of the 16 application conditions listed in

Table 3-2 in Section 3.1 Load Area and Sensor Configuration were repeated with the three

different base materials: Extra Soft, Soft 10A, and Soft 20A. The order of application conditions

selected for testing was randomized to reduce possible testing bias.

3.2.2.2.2 Sensor Conditioning and Testing

Prior to testing each sensor, manufacturer guidelines for sensor conditioning were followed [44],

in which 110% of the maximum test load (11 N) was applied to the sensor for 30 seconds, and

then removed for 30 seconds. This cycle was repeated four times. No data was collected for this

period as the testing was performed solely to condition the sensor before testing.

32

For each of the 48 application conditions, a force sweep from 0 to 10 N was applied to the sensor

at a loading rate of 0.67 N/s (i.e. loading duration of 15 seconds). This force sweep was repeated

three times. The corresponding resistance and force data were curve-fit using an exponential

relationship on MATLAB to characterize the sensors force-resistance curve for each trial [64].

This characteristic curve was then compared across trials and application conditions to study

sensor repeatability.

3.2.2.2.3 Analysis

Sensor performance was assessed using the coefficient of variation (CV) as a measure of sensor

repeatability, as in Section 3.1 Load Area and Sensor Configuration. As stated in the previous

chapter, the Sensitronics manufacturer reports a part-to-part repeatability of 15% and a single

part repeatability of 5%, while the Peratech manufacturer reports only the part-to-part

repeatability (CV) of 4.5% [42], [66]. A CV of 10% is typically acceptable in clinical

applications [31].

An analysis of variance (ANOVA) was used to examine the effects of the various factors

(diameter, backing, puck, and material) on the CV data for each sensor model (i.e., Peratech and

Sensitronics). The specific protocol was described in Section 3.1 Load Area and Sensor

Configuration.

Again, a sub-objective was related to understanding the effects of matching the calibration

settings to the experimental settings. A time-demanding matched-area calibration and a time-

saving, clinically feasible generalized-area calibration method, defined in Section 3.1 Load Area

and Sensor Configuration, were both used. An overall across-configuration CV method was also

calculated, representing the variation across all trials (i.e., all areas and replicates) for a given

configuration (i.e., NBNP, NBYP, YBNP, YBYP with each material). While sensor

manufacturers recommend calibration conditions that mimic sensor use, this is not always

possible in clinical situations at the body-device interface (i.e., inconsistent actuation and area of

applied load); hence the relevance of the generalized-area calibration technique. A pair-wise t-

test was performed on each sensor model comparing the matched-area and generalized-area

calibrations.

33

3.2.3 Results

Figure 3-7 and Figure 3-8 show the characteristic force-resistance curves for the three replicates

of all 48 application conditions for the Peratech and Sensitronics sensors, respectively.

Each figure contains three groupings of subplots for the I) Extra Soft, II) Soft 10A, and III) Soft

20A materials. Within these groupings, subplots are clustered by configuration: i) no rigid

backing or elastomer puck (NBNP), ii) no rigid backing with elastomer puck (NBYP), iii) rigid

backing with no elastomer puck (YBNP), and iv) rigid backing with elastomer puck (YBYP).

Line colours and styles differentiate the area of applied load and trial number, as displayed in the

legend. It should be noted that the y-axes for Figure 3-7 and Figure 3-8 differ, due to differences

in sensor resistance output.

Table 3-6 displays the average coefficient of variation values calculated for each configuration

(I.e., NBNP, NBYP, YBNP, YBYP) and material combination, averaged over the four areas,

calculated for both sensors using both matched-area and generalized-area calibration methods.

Again, matched-area calibration is visualized by the variance in lines of a given colour on a

subplot, generalized-area calibration is visualized by the variance in lines of any given colour

compared with the black lines (8 mm tip) on the same subplot, and the overall CV is visualized

by the variance in all the lines of a given subplot. A red-yellow-green colour gradient was

applied to the cells indicating where each CV falls within the entire table range. The table also

displays the overall across-configuration CV calculated over the four areas and all trials for each

given configuration. Table A-1 in Appendix B displays all data unaveraged from Table 3-6,

highlighting the CV for each area under each application condition.

Overall, the Peratech sensor is significantly more repeatable than the Sensitronics sensor (i.e.,

lower CV), and the matched-area calibration method is significantly more repeatable than the

generalized-area calibration method. The Peratech sensor generalized-area calibration values are

under the 10% desired threshold for all configurations with a puck, regardless of material or

calibration method. For the Sensitronics sensor, CV values averaged 9.4 ± 2.1% for the matched-

area calibration under all configurations, and 11.3 ± 2.1 % for with-puck configurations using the

generalized-area calibration method. The across-configuration CV values approach 100% for no-

puck configurations, however the values remain below 20% for puck conditions.

34

Table 3-7 displays the results (p-values) from the ANOVA test performed on each sensor’s

generalized-area calibration data calculated for the main effects, and 2 and 3-way interactions.

Figure 3-9 and Figure 3-10 display the interaction profiles for the Peratech and Sensitronics

sensors, respectively, using the generalized-area calibration data. As in the previous section, each

cell contains line segments plotted for the interaction of the row effect with the column effect.

Response values predicted by the model are connected by line segments. Non-parallel line

segments indicate potential interactions, and significant interactions are confirmed with the p-

values in Table 3-7. The generalized-area calibration data was selected as it better represents

sensor performance in clinical use, compared to the matched-area calibration data.

35


Peratech sensor.

36


Sensitronics sensor.

37

Table 3-6: Coefficient of variation values for configuration and material for Peratech and

Sensitronics sensors using matched-area, generalized-area and across configuration calibration

methods.

Material Config

Average Coefficient of Variation (%)

Peratech

MA

Peratech

GA

Peratech

Across

Config

Sensitronics

MA

Sensitronics

GA

Sensitronics

Across

Config

Extra

Soft

NBNP 2.9 ± 2.3 27.3 ± 24.0 51.9 ± 27.4 10.4 ± 2.7 37.9 ± 32.6 98.6 ± 55.8

NBYP 0.7 ± 0.2 1.8 ± 0.9 3.7 ± 2.4 11.2 ± 3.0 15.6 ± 5.6 18.4 ± 4.3

YBNP 2.6 ± 2.4 27.4 ± 23.4 55.9 ± 33.3 12.7 ± 6.7 37.0 ± 27.8 107.1 ± 47.3

YBYP 1.0 ± 0.4 3.0 ± 2.0 6.6 ± 1.1 8.1 ± 1.6 10.8 ± 3.1 14.1 ± 5.9

Soft

10A

NBNP 9.0 ± 9.5 25.6 ± 22.5 59.0 ± 65.3 7.5 ± 1.2 42.1 ± 37.7 91.6 ± 38.5

NBYP 0.8 ± 0.2 2.8 ± 1.9 5.1 ± 1.1 8.4 ± 1.8 10.8 ± 3.0 14.5 ± 8.2

YBNP 5.9 ± 6.1 31.2 ± 27.0 84.1 ± 53.0 11.1 ± 2.6 35.9 ± 29.2 104.5 ± 34.6

YBYP 1.6 ± 0.8 4.4 ± 3.4 8.9 ± 0.6 6.4 ± 2.5 8.8 ± 2.5 13.5 ± 8.5

Soft

20A

NBNP 7.2 ± 9.6 25.5 ± 22.8 68.6 ± 62.9 11.1 ± 10.8 18.6 ± 11.8 48.4 ± 59.8

NBYP 1.0 ± 0.3 2.3 ± 1.7 4.6 ± 0.8 7.5 ± 0.8 9.9 ± 2.2 14.5 ± 8.2

YBNP 4.9 ± 5.3 30.6 ± 28.4 77.2 ± 40.9 11.6 ± 11.8 16.5 ± 9.1 56.3 ± 79.5

YBYP 1.4 ± 0.3 3.4 ± 1.5 6.2 ± 1.2 6.8 ± 2.2 12.0 ± 3.7 14.3 ± 7.9

Note: Peratech sensor significantly more repeatable than Sensitronics sensor.

Note: Matched-area calibration significantly more repeatable than generalized-area calibration.

Note: No significant 2-way interaction between sensor and calibration, or 3-way interaction between sensor,

calibration and compliance.

Table 3-7: ANOVA results (p-values) from the factorial analysis performed on each sensor model

using generalized-area calibration data.


Area 0.0005* 0.0015*

Backing 0.7501 0.4152

Puck <.0001* 0.0003*

Compliance 0.7609 0.0053*

Area*Backing 0.3363 0.3266

Area*Puck 0.0005* 0.0016*

Area*Compliance 0.7585 0.0488*

Backing*Puck 0.7919 0.5761

Backing*Compliance 0.5966 0.6755

Puck*Compliance 0.9904 0.0061*

Area*Backing*Puck 0.2014 0.3377

Area*Backing*Compliance 0.4266 0.8148

Area*Puck*Compliance 0.7767 0.0510

Backing*Puck*Compliance 0.6274 0.6250


38

Figure 3-9: Interaction profiles for Peratech sensor using simplified generalized-area calibration

CV values.

Figure 3-10: Interaction profiles for Sensitronics sensor using simplified generalized-area

calibration CV values.

Overall, the Peratech sensor exhibited better repeatability (i.e., lower CV) than the Sensitronics

sensor (p < 0.05), regardless of calibration method. For both sensors, the presence of a puck had

the strongest effect on repeatability, with the presence of the puck improving repeatability. This

can be visualized in the decreased variance in the plots on the right-most side (YP conditions),

compared to the plots on the left-most side (NP) conditions in Figure 3-7 and Figure 3-8. The

39

main effect for area and a two-way interaction between area and puck were also significant for

both sensors. Compliance, area*compliance, and puck*compliance were significant for the

Sensitronics sensor only.

The main effect for area and the two-way interaction between area and puck match the results

from Section 3.1 Load Area and Sensor Configuration, where the larger areas exhibit lower

repeatability, and the presence of the puck improves repeatability for the larger areas.

Compliance did not have a significant effect on the Peratech sensor’s performance. However, for

the Sensitronics sensor, the Soft 20A material was significantly more repeatable than the Extra

Soft and Soft 10A materials. There was no significant difference between the Extra Soft and Soft

10A materials for the Sensitronics sensor.

Two-way interactions between i) compliance and area and ii) compliance and puck were

significant for the Sensitronics sensor. The larger areas realized improved repeatability on the

least soft material (Soft 20A), compared to the two softer materials.

The presence of the backing was not deemed significant for either sensor. Neither the main effect

nor any interaction including the backing was statistically significant.


This section studied the effects of material compliance, area of applied load, and sensor

configuration (elastomer loading puck and thin rigid backing). Two commercially available

FSRs, the Peratech and Sensitronics sensors were evaluated under conditions simulating the

body-device interface. Secondly, the study examined the effects of i) a matched-area and ii) a

more simplified, generalized-area calibration method. This section built upon Section 3.1 Load

Area and Sensor Configuration, featuring the addition of the material compliance factor and an

updated loading protocol. Commercial FSR manufacturer recommendations for rigid surfaces

beneath the sensor and consistent actuation do not represent the conditions present at the body-

device interface, thus necessitating evaluation of the various biomechanical effects at the

interface.

In clinical applications, the area of applied load and tissue compliance are likely to change

dynamically. Therefore, it is important that a sensor perform repeatably over a range of material

40

compliance values, as well as a range of areas. The generalized-area calibration method in this

study simulates clinical use, where sensors may be calibrated using an initial or standard area,

but throughout use, the area is subject to change.

Overall, the compliance, area of applied load, and sensor configuration had significant effects on

sensor repeatability. As in the previous study, the presence of compliant puck had the strongest

effect on repeatability for both sensors, with the puck presence improving repeatability.

With the use of a load puck, the Peratech sensor was able to consistently provide repeatable

results (less than 10% CV) under testing with all three materials, regardless of calibration

method. Under the generalized-area calibration, the Sensitronics sensor was not able to

consistently achieve repeatable results due to the sensor’s sensitivity to area effects, with a CV

ranging from 8.8 ± 2.5 to 42.1 ± 37.7 % across configurations and materials. It is thus

recommended that the Peratech sensor be used with a load puck for interfacial pressure

monitoring at the body-device interface.

While the compliance or material factor was significant, it is important to note the other

configuration conditions. The backing did not have a significant effect, while the puck did,

overall. This indicates the use of a backing is not warranted to improve repeatability over a range

of compliant surfaces, however the use of a puck is. Limitations to the study are included in

Section 5.1.

41

3.3 Dynamic Performance

Evaluating the Dynamic Performance of Interfacial Pressure Sensors at a Simulated Body-

Device Interface

This manuscript was prepared for submission to IEEE Sensors Journal. The limitations and future work

are summarized in Chapter 5 to improve the flow of information and reduce repetition. An abbreviated

version of the methods is presented and provides references to previous sections to reduce repetition.

3.3.1 Introduction

Pressure sensing in MAT devices can help inform the fitting of patient-customized devices like

prosthetics and orthotics. For example, lower-limb prosthetic fitting and alignment can take

multiple weeks from the first to the final optimized fitting [71]. Each fitting session features both

static (i.e. standing) and dynamic (i.e. walking) weight-bearing assessments, where the

prosthetist relies on their visual perception and fitting experience, as well as amputee feedback to

iteratively refine the fit of the device [71]. While both static and dynamic assessments provide

critical information, dynamic assessments are typically preferable as they provide insight to the

device’s functioning in dynamic, everyday use [72].

However, it is increasingly difficult for a prosthetist to visually perceive the breadth of

information observed in a dynamic walking trial compared to a stationary trial. The integration

of pressure-sensing into the prosthetic fitting process has the potential to reduce the subjectivity

in fitting – enabling prosthetist to base decisions off quantifiable data, not simply their own

observations, experience, and the patient’s feedback. The integration of pressure sensing can also

reduce the number of iterations, or the time, between the initial and final optimized fitting, as

well as improve the overall fit of the device.

There are several commercially available interfacial pressure sensors, including the Quantum

Tunneling Composite (QTC™) sensor (Peratech Ltd, Richmond, North Yorkshire, UK) and

others. Commercially available interfacial pressure sensors have several limitations. The force-

resistance relationship of an FSR depends on sensor shape, geometry, and ink formulation, as

well as actuator geometry and rigidity [10]. Thus, the sensor response is highly dependent on

effects of area of applied load, as displayed in Section 3.1. Sensor manufacturers recommend the

area of applied load should be held constant at an area slightly smaller than the sensors’ sensing

42

areas [10], [44], [61]. Consistent actuation is not representative of conditions at the body-device

interface, where the loading area fluctuates and typically is larger than the sensing area.

Furthermore, through dynamic testing, commercial interfacial pressure sensors have reported

high hysteresis errors [4], [35]. Castellini et al. report that there is a trade-off between the

hysteresis error in a sensor and the static sensitivity, as increased stiffness will alter the

viscoelastic behavior causing hysteresis, but reduced static sensitivity [73].

Most studies that have been performed on commercial sensor evaluation focus on static testing.

The few sensor evaluation studies that have examined dynamic loading used simple dynamic

loading patterns [9], [31], [39]. For example, the dynamic loading profiles in studies by Parmar

et al. and Khodasevych et al. were defined as 10 cycles of the application of a load for 30

seconds on and 30 seconds off [31], [39]. This loading pattern was meant to simulate dynamic

wear of a prosthesis; however, it is an overly simplistic model, considering the average stance

time is approximately one second [74]. Thus, there is a need for a dynamic sensor evaluation

protocol simulating MAT wear and use, as well as a study that examines the effects of area of

applied load on the sensor.

The overall objective of this section was to evaluate the effects of load area and elastomer puck

presence on two commercial FSRs’ dynamic performance. A sub-objective of the study was to

understand the effects of two calibration techniques on sensor performance, including calibrating

under matched-area and simplified, generalized-area conditions.

3.3.2 Methods

Testing was performed the understand the effect of area of applied load on sensor dynamic

performance under two loading configurations: with and without a puck.

3.3.2.1 Sensors

The sensors described in 3.1.2 Methods were used in this section. Peratech reports a hysteresis

error of 8.5% for their sensor, while Sensitronics does not report the value [42], [66].

43

3.3.2.2 Data Acquisition System

The data acquisition system described in 3.2.2 Methods for the compliance testing were used in

this section.


An apparatus designed to simulate human tissue was developed for use in a previous study, and

was described in in Section 3.1.2 Methods.

To understand the effects of sensor configuration, a loading puck was used as per sensor

manufacturers’ recommendations under half of the conditions tested. A silicone loading puck

(1.5 mm thickness, 8 mm diameter, and Durometer 60 Shore A hardness) guaranteed the force

was transferred through the sensing area. Previous work (see Section 3.1) indicated the presence

of a rigid backing did not significantly affect sensor performance and was thus not used in this

study.

3.3.3 Protocol

3.3.3.1 Application Conditions

To evaluate the effects of load area and elastomer puck presence on both sensors’ dynamic

performance, a full factorial experiment was conducted using eight application conditions: four

loading tip areas, both with and without an elastomer puck. The order of application conditions

was randomized to minimize potential testing bias.

3.3.3.2 Sensor Conditioning

Prior to testing each sensor, manufacturer guidelines for sensor conditioning were followed [44],

in which 110% of the maximum test load (11 N) was applied to the sensor for 30 seconds, and

then removed for 30 seconds. This cycle was repeated four times.

3.3.3.3 Sensor Calibration

Prior to dynamic testing, a force sweep from 0 to 10 N was applied to the sensor at a loading rate

of 0.67 N/s (i.e. loading duration of 15 s). This force sweep was repeated three times and the

corresponding resistance and force data were curve-fit using an exponential relationship on

MATLAB to characterize the sensors force-resistance curve for a given configuration (i.e. area

44

and puck configuration) [64]. This equation was used to convert resistance output to pressure

values for the subsequent tests.

Two different calibration methods were used: matched-area and generalized-area calibration

techniques. Though sensor manufacturers recommend calibration conditions that imitate sensor

use, this is not always possible at the body-device interface (i.e., inconsistent actuation and area

of applied load); showcasing the importance of the simplified generalized-area calibration

method. Detailed in Section 3.1, the matched-area calibration method is a more accurate, time-

consuming approach that matches calibration settings to experimental settings, and the

generalized-area calibration is a clinically-relevant time-saving approach that applies the

calibration data from one configuration (standard 8 mm area) to multiple experimental

conditions. For this study, both calibration methods were used to convert sensor resistance to

force, enabling a comparison between the result parameters (i.e., NRMSE and HE). Regarding

the matched-area calibration, the calibration equation for each matched-area configuration was

applied to the experimental data. For the generalized-area calibration, the calibration equation

from the 8 mm puck and the given puck configuration was applied to each area.

3.3.3.4 Sensor Testing

3.3.3.4.1 Hysteresis Testing

Hysteresis is the difference in sensor output at the same force when the sensor is being loaded

and unloaded and is commonly used to assess the performance of FSRs [45], [65]. To understand

the sensor’s dynamic performance and identify hysteresis effects, the sensor was loaded from 0

to 10 N and then unloaded to 0 N at a rate of 10 N/s (duration of two seconds). This loading rate

was selected to analyze the hysteresis effects under conditions similar to dynamic loading: one

second each of loading and unloading in the test is comparable to the average stance time of

approximately one second [74].

3.3.3.4.2 Dynamic Simulated Gait Testing

Simulating gait, a dynamic profile, developed as an objective of the study, was applied to the

sensor: loaded to 10 N, held for 1 second, unloaded to 0.5 N, held for 1 second, repeated 10

times. The loaded section of the cycle represents the stance phase of gait, while the unloaded

section represents swing phase. The 10-cycle series represents a 10-step cycle through gait. This

45

test was included as gait trials are integral to fitting sessions for customized MAT specifically, as

well as daily living with MAT.

3.3.3.5 Analysis

Sensor performance was evaluated through analyzing the following measures: accuracy and

hysteresis. Researchers identified these imperative parameters during the evaluation of an

interface force/pressure sensor [39], [41], [45]. Accuracy errors, evaluated in both hysteresis and

gait testing, was calculated using a normalized root-mean squared error (NRMSE). The equation

used to calculate RMSE:

𝑅𝑀𝑆𝐸 =√

∑ (𝐹𝑎𝑝𝑝𝑙𝑖𝑒𝑑 − 𝐹𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑)2𝑛

𝑖=1

𝑛

where in this analysis, n is an array of time values beginning at 0 seconds and increasing by

0.002 second increments for the duration of the test. The normalized RMSE, or NRMSE, is then

calculated by dividing by applied force of 10 N and then converting the value to a percentage:

𝑁𝑅𝑀𝑆𝐸 =𝑅𝑀𝑆𝐸

𝐹 ̅∗ 100%

Hysteresis error (HE), evaluated through the hysteresis testing, was calculated by taking the

maximum difference in sensor output (loading versus unloading) for a given force level. The

hysteresis difference, Funloading – Floading, was calculated at each force from 0.5 to 10 N at

increments of 0.1 N. The equation used to calculate HE:

𝐻𝐸 =𝐹𝑢𝑛𝑙𝑜𝑎𝑑𝑖𝑛𝑔 − 𝐹𝑙𝑜𝑎𝑑𝑖𝑛𝑔

𝐹 ̅ ∗ 100%

The hysteresis error was normalized by dividing by the maximum force of 10 N, and then was

converted to a percentage. For each trial, the hysteresis error was calculated at the force with the

greatest hysteresis difference.

An analysis of variance (ANOVA) was used to compare the effects of calibration method and

puck on the NRMSE and HE for each sensor model (i.e., Peratech and Sensitronics). All main

effects, 2-way and 3-way interactions were evaluated with p<0.05 indicating significance.

Insignificant effects were then removed from the model, and significant main effects, 2 and 3-

46

way interactions were reported. JMP® Pro 14 software was used (SAS Institute Inc., Cary, NC,

USA). A paired t-test was performed on each set of results (i.e., NRMSE, HE) to quantify

differences between sensor performance.

As part of the sub-objective, two different calibration methods were used in this study to

determine the effects of calibration. A pair-wise t-test was performed on each sensor model

comparing the matched-area and generalized-area calibrations.

3.3.4 Results

3.3.4.1.1 Dynamic Hysteresis Testing

The force applied vs. time plots for the Peratech and Sensitronics sensors are displayed in Figure

3-11 and Figure 3-12, respectively. The transformed force applied vs. calculated force

representing the hysteresis curves for the Peratech and Sensitronics sensors are displayed in

Figure 3-13 and Figure 3-14, respectively. Subplots are grouped by configuration: left) no rigid

backing or elastomer puck (NBNP), and right) no rigid backing with elastomer puck (NBYP); as

well as calibration method: matched-area (subplots i and ii) and generalized-area calibration

(subplots iii and iv). Line colours distinguish the area of applied load, and line styles distinguish

the trial number, as shown in the legend. NRMSE and HE values for the applications conditions

are displayed in Table 3-8.

In Figure 3-11 and Figure 3-12, the applied force waveform is displayed in green on the plot, as

indicated in the legend. These figures provide a visualization of the sensor’s accuracy in each

configuration. Overall, the Peratech sensor exhibits higher accuracy than the Sensitronics sensor.

The Sensitronics sensor signal exhibits much more noise. The addition of the puck shows a

significant improvement in sensor accuracy, especially in the generalized-area calibration data.

Without a puck, the generalized-area calibration data accuracy reaches roughly 50% for the

larger areas with both sensors. A dead band appears for the Peratech sensor in the NBNP

condition with areas larger than the sensing area (15 and 25 mm), where no force is measured

until approximately 3.5 N. In the matched-area calibration settings, following the dead band, the

data reaches 10 N because each data set was calibrated individually, with a different set of

resistance values corresponding the force values for each configuration. This dead band is further

explained in Appendix A: Explaining the Deadband in Hysteresis Data.

47

Regarding the hysteresis plots in Figure 3-13 and Figure 3-14, the plot for a sensor exhibiting

zero hysteresis would feature a linear segment with a slope of 1, identical for loading and

unloading segments. The difference between loading and unloading curves is thus hysteresis

error. Overall, the hysteresis error is significantly less for Peratech sensor, compared to the

Sensitronics sensor (p=0.01). For both sensors, the addition of the puck appears to reduce

hysteresis error, as the right-most plots (NBYP) display less variance than the left-most plots.

The dead band described above can also be seen in these hysteresis plots. Overall, the effect of

calibration method does not have a significant effect on hysteresis error.

Figure 3-11: Force vs. time plots for two-second hysteresis tests for Peratech sensor using matched-

area (subplots i and ii) and generalized-area (subplots iii and iv) calibration methods.

48

Figure 3-12: Force vs. time plots for two-second hysteresis tests for Sensitronics sensor using

matched-area (subplots i and ii) and generalized-area (subplots iii and iv) calibration methods.

Figure 3-13: Force measured vs. force applied plots for two-second hysteresis tests for Peratech

sensor using specific matched-area (subplots i and ii) and simplified generalized-area (subplots iii

and iv) calibration methods.

49

Figure 3-14: Force measured vs. force applied plots for two-second hysteresis tests for Sensitronics

sensor using specific matched-area (subplots i and ii) and simplified generalized-area (subplots iii

and iv) calibration methods.

Table 3-8: Hysteresis error and NRMSE from hysteresis test using both matched-area and

generalized-area calibration for both Peratech and Sensitronics sensors.

Config Area

Peratech (%) Sensitronics (%)

MA

Calibration

GA

Calibration

MA

Calibration

GA

Calibration

NRMSE HE NRMSE HE NRMSE HE NRMSE HE

NBNP

05 2.6 ± 1.3 8.4 ± 1.7 32.8 ± 0.1 15.9 ± 4.6 5.4 ± 0.5 15.9 ± 1.4 7.8 ± 1.7 20.6 ± 2.8

08 2.0 ± 0.2 8.6 ± 0.4 2.0 ± 0.2 8.6 ± 0.4 5.4 ± 2.1 16.0 ± 4.5 5.4 ± 2.1 16.0 ± 4.5

15 11.1 ± 0.2 23.2 ± 15.3 46.8 ± 0.1 12.8 ± 3.9 9.9 ± 0.2 43.2 ± 8.6 57.3 ± 0.1 39.8 ± 14.6

25 11.0 ± 0.1 12.2 ± 1.6 52.2 ± 0.3 11.5 ± 4.2 11.7 ± 2.1 48.3 ± 7.3 54.9 ± 0.3 38.6 ± 13.2

NBYP

05 4.8 ± 0.4 13.4 ± 0.8 9.2 ± 1.0 15.2 ± 0.6 5.3 ± 1.6 16.8 ± 2.9 9.9 ± 0.4 18.7 ± 2.7

08 1.7 ± 0.4 7.8 ± 1.4 1.7 ± 0.4 7.8 ± 1.4 9.5 ± 0.9 25.4 ± 1.4 9.5 ± 0.9 25.4 ± 1.4

15 2.5 ± 0.2 9.5 ± 0.4 2.8 ± 0.2 8.0 ± 0.1 7.3 ± 1.8 19.8 ± 2.5 9.8 ± 0.7 20.9 ± 1.9

25 2.0 ± 0.3 8.8 ± 0.9 3.0 ± 0.1 6.6 ± 1.0 6.6 ± 2.2 19.0 ± 5.4 8.7 ± 1.1 22.4 ± 3.8

Table 3-9: ANOVA results (p-values) for both Peratech and Sensitronics sensors from hysteresis

test results.

Effect Peratech

NRMSE

Peratech

HE

Sensitronics

NRMSE

Sensitronics

HE

Area <.0001* 0.0241* <.0001* <.0001*

Puck <.0001* 0.025* <.0001* 0.0001*

Calibration <.0001* 0.7189 <.0001* 0.8765

Area*Puck <.0001* 0.0187* <.0001* <.0001*

Area*Calibration <.0001* 0.0382* <.0001* 0.721

Puck*Calibration <.0001* 0.8267 <.0001* 0.5411


50

For both sensors, the NRMSE is below 10% for the majority of cases, with most exceptions

occurring under conditions without a loading puck at the larger areas. While the HE is under

10% for most conditions for the Peratech sensor, all the HE values for the Sensitronics sensor are

above 10% recommended for use in clinical applications [31].

3.3.4.1.2 Dynamic Simulated Gait Testing

The force-time plots for the gait tests are displayed in Figure 3-15 and Figure 3-16 for the

Peratech and Sensitronics sensors, respectively. Normalized root-mean-squared error values for

both sensors under the different application conditions are displayed in Table 3-10. With the

matched-area calibration method, the NRMSE is below 10% for both sensors, meeting the

desired threshold for accuracy in clinical applications [31]. Using the generalized-area

calibration method with a puck, the CV across the areas averaged 8.8 ± 3.8 % for both sensors.

Without a puck for the generalized-area calibration method, CV values exceed 100% for the

larger areas for both sensors.

Figure 3-15: Force vs. time plots from dynamic gait testing tests for Peratech sensor using specific

matched-area (subplots i and ii) and simplified generalized-area (subplots iii and iv) calibration

methods.

51

Figure 3-16: Force vs. time plots from dynamic gait testing tests for Sensitronics sensor using specific

matched-area (subplots i and ii) and simplified generalized-area (subplots iii and iv) calibration

methods.

Table 3-10: Gait simulation testing error values for both Peratech and Sensitronics sensors.

Configuration Area

NRMSE – Gait (%)

Peratech Sensor Sensitronics Sensor

MA

Calibration

GA

Calibration

MA

Calibration

GA

Calibration

NBNP

05 3.7 ± 1.3 112.9 ± 2.8 8.9 ± 1.6 6.8 ± 0.9

08 6.2 ± 0.3 6.2 ± 0.3 5.0 ± 0.8 5.0 ± 0.8

15 6.6 ± 0.8 118.0 ± 5.9 8.4 ± 0.4 317.9 ± 18.3

25 6.1 ± 0.2 558.3 ± 16.6 5.4 ± 0.6 185.6 ± 6.5

NBYP

05 4.8 ± 0.1 17.2 ± 0.0 4.2 ± 0.8 11.5 ± 1.3

08 4.9 ± 0.6 4.9 ± 0.6 6.8 ± 0.4 6.8 ± 0.4

15 3.7 ± 0.2 6.3 ± 0.3 5.9 ± 0.7 6.3 ± 0.8

25 2.9 ± 0.1 6.8 ± 0.5 7.3 ± 0.1 10.8 ± 1.2

Table 3-11: ANOVA results (p-values) from gait simulation testing results.


Area <.0001* <.0001*

Puck <.0001* <.0001*

Calibration <.0001* <.0001*

Area*Puck <.0001* <.0001*

Area*Calibration <.0001* <.0001*

Puck*Calibration <.0001* <.0001*


52


Understanding the dynamic performance of commercial FSRs at body-device interfaces is

critical to support their use in the fitting, prescription, and evaluation of MAT devices. This

study focused on the application of novel dynamic loading, configuration, and calibration

techniques to characterize and evaluate dynamic commercial FSR performance. Previous sensor

evaluation studies failed to examine sensors under dynamic conditions representative of gait at

the body-device interface.

While consistent actuation and rigid mounting is recommended by sensor manufacturers, the

human body exhibits compliant tissue and dynamic actuation [10]. Furthermore, many

researchers have cited hysteresis and accuracy as key requirements of a successful interfacial

sensor, although performance under dynamic conditions is often a limitation [39], [41], [45].

Hysteresis effects, present in dynamic conditions when the sensor is being consistently loaded

and unloaded, reduce the overall accuracy of the sensor. This study determined the hysteresis and

accuracy errors under various conditions representing dynamic loading and used these

parameters to compare sensor models under various configurations.

Overall, the calibration method had a very significant effect on sensor performance, but this is

mitigated with the use of a load puck. The highest errors occurred under conditions with the

larger areas in the absence of a loading puck. This is likely due to the load being transmitted to

surrounding tissue instead of entirely through the sensing area, as described in Section 3.1. The

dead band in the Peratech sensor 15 and 25 NBNP conditions exemplifies this load transmission

complication. Without the puck, the larger area load applicators transmit the load primarily

through the silicone surrounding the sensor, then the sensor’s adhesive layer, and lastly the

sensing area. In these conditions at low forces, the load transmitted through the sensing area was

negligible, and did not produce a change in resistance from the sensor. At a force of

approximately 3.5 N, the surrounding tissue was compressed enough to enable the minimum

activation force to be applied to the sensing area, initiating the curve. The dead band may not

have occurred with the Sensitronics sensor because the sensing area is larger on the Sensitronics

sensor, so more force would be transmitted to it than the Peratech sensor.

Regardless of calibration method, the hysteresis error is significantly lower for the Peratech

sensor (p=0.01) than the Sensitronics sensor. Other improved performance parameters include

53

reduced noise, and higher accuracy overall compared to the Sensitronics sensor. This is likely

due to the chemical formulation of the sensing material, as the Peratech sensor uses a newly

developed QTC™ composite, and the Sensitronics uses a more conventional piezoresistive

material.

Overall, the Peratech sensor provides accurate pressure measurement under both the puck and no

puck configuration conditions for the hysteresis testing using the matched-area calibration

method. With a puck, the hysteresis errors remain below 10% for three of the four areas, and

under 15% for the fourth area. With a puck, this sensor could be used reliably for use at the

body-device interfaces due to the robustness of the sensor’s performance – its reliable

performance regardless of area of applied load and other configuration factors.

Regarding the dynamic gait simulation testing, both sensors exhibit low errors using the

matched-area calibration method. However, the matched-area calibration method is not

necessarily feasible in clinical settings. With the generalized-area calibration method, the use of

a load puck must be used to achieve errors near 10%.

Overall, the dynamic performance of the two commercial sensors is quite similar, however the

Peratech overall exhibited improved performance (i.e. higher accuracy) with lower hysteresis

errors. As in Section 3.1, the authors recommend the use of a load puck under dynamic

conditions to ensure the load is transmitted through the sensor’s sensing area. The use of the

generalized-area calibration method is warranted, given the use of a loading puck.

The NRMSE values reported in this study agree with the accuracy values reported in the study

by Parmar et al [31]. Parmar et. al reported average accuracy in dynamic pressure measurements

ranging from 94.8 to 96.0% (equivalent to an error of 4.0 to 5.2%) for the Peratech sensor and

90.8 to 94.0% (equivalent to an error of 6.0 to 9.2%) for the Sensitronics sensor [31]. These

values are within the range of the errors reported in the 8 mm loading tip applicator conditions

using matched-area calibration. The hysteresis error observed for the Peratech sensor with the 8

mm loading tip applicator conditions using matched-area calibration also agrees with the

manufacturer reported hysteresis error of 8.5% [75]. Though the findings for this sub-set of

configurations agree with previous reports, the information for the range of application-relevant

factors including area and calibration, as well as their interactions, provides new information that

has not been previously explored. Limitations to the study are included in Section 5.1.

54

Chapter 4

QTC Prototype Sensor Evaluation

Development and Evaluation of a Printable Sensor for Pressure-Mapping at Body-Device

Interfaces

4.1 Introduction

A pressure sensor designed to be 3D-printed and embedded within MAT is being developed by

Dr. Kamran Behdinan’s ARL-MLS (Advanced Research Laboratory for Multifunctional

Lightweight Structures) and Dr. Jan Andrysek’s PROPEL (Paediatrics, Rehabilitation, Orthotics,

Prosthetics, Engineering, Locomotion) labs. The device is a quantum tunneling composite

(QTC) pressure sensor in the initial stages of design and development, consisting of a silicone-

nickel composite. The current sensing prototype is fabricated using manual methods, and its

performance is yet to be fully explored. The overall aim of this chapter is to evaluate the

performance of the QTC prototype sensor at a simulated clinically relevant body-device interface

(i.e. lower-limb prosthetic socket), and to compare the sensor to commercially available

interfacial pressure sensors. Upon validation of this sensor prototype, the two labs will begin

investigating 3D-printing this sensor (outside the scope of this thesis).

QTC consists of nickel nanoparticles immersed within an elastomeric polymer matrix. At rest,

the polymer acts as an insulator as the metal particles are not in contact with each other. Upon

deformation, the nanoparticles approach each other, causing the electrical resistance to drop by

several orders of magnitude [43]. Quantum tunneling is achieved by precisely controlling the

shape and blending process used with the nanoparticles. Peratech Ltd. has commercialized

several thin-film QTC pressure sensors that have demonstrated excellent performance [31], [39].

4.1.1 Sensor Fabrication

The QTC prototype sensor was constructed from a spiky spherical nickel (Ni) (T123™, Vale

S.A., Rio de Janeiro, Brazil), carbon black (CB) (ENSACO ® 260 G, Imerys S.A., Paris,

France), and Polydimethylphenylsiloxane (PDMS) (SYLGARD™ 184 Silicone Elastomer, Dow

Chemical Company, Midland, Michigan, United States) composite using a Ni-CB-PDMS mass

ratio of 4:0.14:1 [76]. The sensor chemical composition and development was performed by the

55

ARL-MLS lab and was outside the scope of this thesis. The sample was shaped with a diameter

of 10 mm and a thickness of 1 mm.

4.2 Methods

The methods based off the static compliance testing in Section 3.2 and the dynamic testing in

Section 3.3 were performed on the QTC prototype sensor.

4.2.1 Testing Apparatus

Figure 4-1 displays the testing apparatus used to evaluate the QTC prototype sensor. Thin copper

sheets (0.0025 mm thickness) were cut to match round sensor area and placed on either side of

the sensor. The resistance was measured across the two copper leads using a Keithley 6500

DMM, as described in Section 3.2.

Figure 4-1: Testing apparatus for QTC prototype sensor.

For the calibration, dynamic hysteresis (15-second test), and gait testing, the 16 applications

conditions used match the application conditions outlined in Table 3-2. For the dynamic

hysteresis testing, the two-second tests were performed using 8 application conditions, matching

the configurations used in Section 3.3 without a rigid backing.

56

4.2.2 Protocol

4.2.2.1 Calibration

A force-sweep from 0-10N was applied over 15 s to the sensor under the 16 different application

conditions. See detailed calibration protocol description in Section 3.2.2 Methods.

4.2.2.2 Dynamic Hysteresis Testing

The two-second hysteresis loading protocol outlined in Section 3.3 was repeated for the QTC

prototype sensor. A preliminary analysis of the two-second hysteresis test using the NBNP and

NBYP configurations revealed a slower sensor response time compared to the commercial

sensors. The hysteresis test was then repeated over a fifteen-second period to further understand

the sensor’s transient response, using all four backing and puck configurations (i.e., NBNP,

NBYP, YBNP, YBYP). The fifteen-second period was selected as it is representative of the time

taken during a 10-step gait assessment, standing trial, or rehabilitation exercise [77].

4.2.2.3 Dynamic Simulated Gait Testing

The dynamic gait loading protocol outlined in Section 3.3 was repeated for the QTC prototype

sensor, using all four backing and puck configurations.

4.2.3 Analysis

The analysis in this chapter was performed following the analysis under Section 3.3. Sensor

performance was again evaluated through accuracy and hysteresis parameters, and secondly,

comparisons were performed across calibration methods. However, for the QTC prototype

sensor, a paired t-test was also performed on each set of hysteresis test results (two and fifteen-

second tests) to quantify differences in sensor performance.

4.3 Results

4.3.1 Calibration

The force-resistance plots from the calibration tests are displayed in Figure 4-2. Subplots are

grouped by configuration: i) no rigid backing or elastomer puck (NBNP), ii) no rigid backing

with elastomer puck (NBYP), iii) rigid backing with no elastomer puck (YBNP), and iv) rigid

backing with elastomer puck (YBYP). Line colours and styles differentiate the area of applied

57

load and trial number, as indicated in the legend. As described in Section 3.1.3 Results, the

matched-area CV is represented by the variance in lines of a given colour on each subplot, and

the generalized-area CV is represented by the variance in the lines of a given colour (any one

area) compared to the black lines (standard 8 mm diameter) on each subplot. The across

configuration CV represents the variance in all lines of a given subplot. The coefficient of

variation values are displayed in Table 4-1. The errors displayed represent the standard

deviation.

Figure 4-2: Characteristic resistance vs. force curves from QTC prototype sensor calibration using

16 application conditions.

58

Table 4-1: Coefficient of variation values for 16 application conditions.

Configuration Area Matched-Area

CV (%)

Generalized-Area

CV (%)

Across Configuration

CV (%)

NBNP

05 4.7 ± 6.5 14.9 ± 14.7

19.9 ± 14.7 08 6.3 ± 1.7 6.3 ± 1.7

15 9.9 ± 11.2 16.2 ± 7.7

25 11.0 ± 5.6 13.5 ± 4.1

NBYP

05 8.8 ± 19.9 20.5 ± 18.6

28.6 ± 12.8 08 4.3 ± 3.4 4.3 ± 3.4

15 7.0 ± 8.2 8.0 ± 12.4

25 10.1 ± 3.5 21.5 ± 3.1

YBNP

05 2.8 ± 3.6 11.3 ± 10.3

13.8 ± 11.6 08 4.3 ± 6.2 4.3 ± 6.2

15 13.6 ± 11.9 13.2 ± 10.8

25 3.0 ± 1.5 6.5 ± 5.8

YBYP

05 5.7 ± 7.9 21.1 ± 7.0

20.7 ± 10.6 08 6.1 ± 3.3 6.1 ± 3.3

15 4.9 ± 2.8 25.7 ± 6.1

25 4.7 ± 2.3 16.2 ± 3.7

Table 4-2: ANOVA results (p-values) using both matched-area and

generalized-area calibration data.

Effect QTC Matched-Area QTC Generalized-Area

Backing 0.143 0.962

Area*Puck 0.178 0.511

Area*Backing 0.245 0.347

Area 0.245 0.088

Puck 0.684 0.122

Backing*Puck 0.959 0.180


Overall, neither the effects of the backing, area, puck, nor the interactions of each combination

were statistically significant (p>0.05).

4.3.2 Dynamic Hysteresis Testing

Figure 4-3 and Figure 4-4 display the force vs. time and measured force vs. applied force plots

for the two-second hysteresis test, respectively. For both figures, subplots are grouped by

configuration and calibration: i) NBNP Matched-Area Calibration, ii) NBYP Matched-Area

Calibration, iii) NBNP Generalized-Area Calibration, and iv) NBYP Generalized-Area

Calibration. The NBNP appears to have slightly higher accuracy than the NBYP condition for

the matched-area calibration data. For the generalized-area calibration data, there does not appear

59

to a difference across configurations, except for the 5 mm diameter tip appears to have high

errors under the NBNP configuration. Hysteresis errors do not appear to be significantly different

across configurations and areas, as the overall distance between loading and unloading curves

(i.e., visual representation of hysteresis error) remains relatively constant.

Figure 4-3: Force vs. time plots for two-second hysteresis tests for QTC prototype sensor using


calibration methods.

60

Figure 4-4: Force measured vs. force applied plots for two-second hysteresis tests for QTC

prototype sensor using specific matched-area (subplots i and ii) and simplified generalized-area

(subplots iii and iv) calibration methods.

Figure 4-5 and Figure 4-6 display the force vs. time plots for the fifteen-second hysteresis test

using matched-area and generalized-area calibration methods, respectively. Figure 4-7 and

Figure 4-8 display the measured force vs. applied force for the same data sets as Figure 4-5 and

Figure 4-6. For the figures, subplots are grouped by configuration: i) NBNP, ii) NBYP, iii)

NBNP, and iv) NBYP. As in the other sections, line colour and style represent the load tip area

and trial number, as indicated in the legend.

Table 4-3 displays the CV calculated using both matched-area and generalized-area calibration

methods from both two and fifteen-second tests on the QTC prototype sensor.

61


using the matched-area calibration method.


using the generalized-area calibration method.

62


prototype sensor using the matched-area calibration method.


prototype sensor using the generalized-area calibration method.

63

For the fifteen-second hysteresis test data, accuracy (displayed through NRMSE) and hysteresis

errors appear relatively constant across configurations and calibration methods.

Table 4-3: Hysteresis error and NRMSE from two and fifteen-second hysteresis tests using both

matched-area and generalized-area calibration methods.

Config Area

2 s Hysteresis Test 15 s Hysteresis Test

MA

Calibration

GA

Calibration

MA

Calibration

GA

Calibration

NRMSE HE NRMSE HE NRMSE HE NRMSE HE

NBNP

05 13.0 ± 2.7 27.0 ± 5.6 9.4 ± 1.1 24.5 ± 4.3 9.4 ± 1.1 24.5 ± 4.3 8.0 ± 1.0 19.0 ± 4.2

08 14.3 ± 2.3 37.6 ± 7.9 7.1 ± 1.7 16.6 ± 3.7 6.9 ± 1.7 16.3 ± 3.7 6.9 ± 1.7 16.3 ± 3.8

15 12.6 ± 1.7 34.8 ± 3.6 12.9 ± 1.0 31.3 ± 4.1 10.4 ± 0.7 23.6 ± 0.9 18.0 ± 1.5 19.7 ± 1.4

25 12.9 ± 1.5 36.6 ± 2.1 12.8 ± 3.9 30.9 ± 8.4 12.8 ± 3.9 30.9 ± 8.4 11.3 ± 1.3 16.9 ± 7.0

NBYP

05 9.5 ± 0.7 20.5 ± 4.2 9.6 ± 0.8 25.1 ± 1.7 9.6 ± 0.8 25.1 ± 1.7 23.2 ± 1.8 24.6 ± 3.6

08 15.6 ± 2.3 30.6 ± 3.8 8.9 ± 0.2 26.2 ± 1.0 8.1 ± 0.2 23.8 ± 1.0 8.1 ± 0.2 23.8 ± 1.7

15 12.3 ± 0.6 40.0 ± 3.7 9.4 ± 0.8 27.9 ± 0.2 9.4 ± 0.8 27.9 ± 0.2 6.9 ± 0.8 20.8 ± 1.9

25 12.4 ± 0.8 28.9 ± 5.4 16.8 ± 3.1 26.8 ± 5.4 16.8 ± 3.1 26.8 ± 5.4 12.3 ± 0.3 31.5 ± 6.5

YBNP

05 9.2 ± 2.0 26.5 ± 4.8 11.9 ± 0.3 27.7 ± 2.0

08 11.2 ± 2.2 26.1 ± 2.9 11.2 ± 2.0 26.1 ± 2.2

15 12.4 ± 0.9 28.7 ± 2.7 12.0 ± 0.7 26.6 ± 2.9

25 7.8 ± 0.4 22.3 ± 1.2 7.5 ± 0.1 23.3 ± 1.4

NBYP

05 9.4 ± 1.0 23.5 ± 2.1 16.9 ± 2.2 23.9 ± 4.0

08 6.9 ± 0.9 19.9 ± 1.7 6.9 ± 0.8 19.9 ± 1.6

15 11.0 ± 1.4 26.7 ± 1.6 15.8 ± 2.6 16.3 ± 2.4

25 8.6 ± 3.6 25.4 ± 5.1 15.2 ± 0.9 30.1 ± 1.9

Comparing results across the two and fifteen second tests, all errors were lower for the fifteen-

second test compared to the two-second test (p<0.05). Comparing results between calibration

methods, significant differences (p<0.05) were observed between two-second NRMSE

(matched-area resulted in lower errors), and fifteen-second HE (generalized-area resulted in

lower errors). All other parameters were not significantly different across calibration methods.

4.3.3 Dynamic Simulated Gait Testing

Figure 4-9 and Figure 4-10 display the force vs. time profiles from dynamic gait testing

calculated using the matched-area and generalized-area calibration methods, respectively.

Table 4-4 displays the NRMSE for the dynamic simulated gait testing calculated using both

matched-area and generalized-area calibration data. The generalized-area calibration data results

in significantly higher errors than the matched-area calibration, however neither calibration

64

method achieves high accuracy. Overall, none of the four backing-puck configurations realize

consistent errors below 10%, regardless of calibration method.

Figure 4-9: Force vs. time plots from dynamic gait testing calculated using matched-area

calibration data.

Figure 4-10: Force vs. time plots from dynamic gait testing calculated using generalized-area

calibration data.

65

Table 4-4: Gait simulation testing error values for QTC prototype sensor.

Configuration Area

NRMSE – Gait (%)

Matched-Area

Calibration

Generalized-Area*

Calibration

NBNP

05 13.6 ± 1.1 16.4 ± 0.8

08 15.3 ± 4.0 15.3 ± 4.0

15 25.8 ± 1.6 35.6 ± 0.8

25 18.6 ± 1.5 28.9 ± 1.2

NBYP

05 17.1 ± 0.9 6.2 ± 0.9

08 13.0 ± 0.2 13.0 ± 0.2

15 19.5 ± 2.3 20.3 ± 0.9

25 6.7 ± 0.3 15.2 ± 0.9

YBNP

05 26.8 ± 1.1 25.8 ± 0.6

08 15.2 ± 0.7 15.2 ± 0.7

15 17.0 ± 1.0 21.4 ± 1.0

25 18.1 ± 1.5 20.0 ± 1.5

YBYP

05 9.1 ± 2.5 25.9 ± 2.6

08 20.8 ± 1.9 20.8 ± 1.9

15 12.6 ± 0.8 31.3 ± 0.9

25 13.5 ± 0.7 27.7 ± 0.7

*Indicates significantly different from matched-area calibration.

4.4 Discussion and Conclusions

For calibration results, the generalized-area CV and across-configuration CV values provide the

most realistic representation of the sensor’s performance in clinical use. Calibration during

clinical use will likely be brief, and the area of applied load may change dynamically. With

across-configuration CV values ranging from 13.8 ± 11.6 to 28.6 ± 12.8%, the desired 10%

threshold for use in clinical applications was not attained [31]. Further revisions to the sensing

prototype are recommended to achieve repeatable results.

Over the two-second hysteresis testing, the sensor displayed a delayed time response. Firstly, the

peak force measured by the sensor was not aligned with the peak applied force. Secondly, the

sensor’s peak measured response did not reach 10 N under any condition. This lengthy sensor

response time could be attributed to the thickness of the sample (1 mm), compared to Peratech’s

sensing thickness of 8 to 10 µm [42]. The increased thickness creates a greater distance for the

electrons to travel. This delayed response was also displayed though the high error rates in the

two-second hysteresis and gait testing. Both tests were performed at a high loading rate and

frequency that did not provide the sensor with enough time to respond. The improved accuracy

displayed in the fifteen-second hysteresis tests also validate this theory. Under the fifteen-second

66

hysteresis tests, the QTC prototype sensor’s accuracy approaches 10% over most conditions,

indicating its feasibility as a sensing prototype.

The configuration (i.e., presence of a backing and puck) did not have a strong effect on the QTC

prototype sensor’s performance. As the entire sensor is constructed of the compliant sensing

component, the loading puck does not aid in ensuring transmission through the sensing area.

Additionally, the sample was relatively thick and minimal bending occurred throughout testing,

so the presence of a rigid backing also had a negligible effect.

The generalized-area calibration method resulted in a higher normalized root mean squared

errors for both hysteresis and gait tests compared to the matched-area calibration method, as

expected. However, the error rates for both calibration methods exceed the desired 10%

threshold [31].

In general, calibration influenced accuracy; however, it did not affect hysteresis error. This is

expected, as the calibration method will change the equation converting resistance to force but

does not affect the difference in sensor output between loading and unloading curves.

Other studies published on the development of new sensors typically present results that compete

with commercially available products, with accuracy typically above 90% [78]. Future

development is required on the current sensing prototype to achieve competitive performance.

Limitations and future work are described in Section 5.1.

67

Chapter 5

Discussion and Conclusions

The methods developed and applied in this work were used for evaluating and characterizing

pressure sensor performance at clinically relevant body-device interfaces. This study introduced

the evaluation of various clinically relevant factors on sensor performance – including the effects

of load area, sensor configuration, tissue compliance, and calibration method. A dynamic loading

protocol was also developed, simulating gait. The conducted evaluation of the commercial

Peratech and Sensitronics sensors can be used to inform the design of pressure-mapping mobility

assistive technology. The evaluation of the QTC prototype sensor provides a preliminary

analysis of its performance, informing future iterations of the design. The contributions of this

work regard to both methodology and sensor development.

Through the static evaluation of the commercial sensors, the effects of load area, sensor

configuration, and tissue compliance were examined. While the area of applied load and tissue

compliance both have significant effects on sensor repeatability, this can be mitigated using a

load puck. The use of a backing is not warranted for the conditions simulated (i.e. uniform

tissue). Through the dynamic evaluation, the presence of the puck was again proven critical.

Overall, the Peratech sensor provided more repeatable, more accurate measurements (i.e., lower

CV, HE, and NRMSE) than the Sensitronics sensor under the various configurations, for both

static and dynamic loading. The secondary objective, evaluating the effects of calibration using

exact and approximated conditions, indicated that a generalized-area calibration technique can be

used in pair with a load puck, and the Peratech sensor is recommended. The Peratech sensor

proved to have the most robust design, and its performance remained consistent when subjected

to the various configurations.

The evaluation of the novel sensor provided valuable information to guide in designing future

iterations of the sensing prototype. The sensor configuration (i.e. backing and puck) and area of

applied were not significant to sensor performance. This is due to the current prototype’s

composition being entirely a sensing component, unlike commercial sensors with adhesive non-

sensing edges. While the matched-area calibration method had more repeatable and higher

accuracy results, both calibration methods had similar results (exceeding desired 10% error

threshold). While this sensor does not currently compete with the performance of the commercial

68

sensors, the sensing prototype has shown significant potential. The potential to 3D-print and

integrate the QTC prototype sensor will be extremely valuable for MAT applications.

5.1 Limitations and Future Work

Limitations to the study should be noted. Firstly, frictional shear forces were not accounted for,

and are assumed to be minimal given the perpendicular loading. However, shear forces can have

significant effects at the body-device interface in real-life applications. A study by Zhang et. al

concluded that both shear and normal stresses have roughly the same effects on tissue, and it is

important to consider the resultant, or combined, stress vector [52]. Secondly, temperature and

curvature effects, which have been examined in other works [39], [64], were not examined in this

study to allow the research question to prioritize the effects of area of applied load and

configuration.

Improved sensor performance was observed using the dynamic force-ramp loading method

(absence of drift) in Section 3.2 compared to the static loading at multiple force levels (presence

of drift) method used in Section 3.1. This displays the errors introduced by drift. Drift was not

included in either study because it has been studied extensively by other works, however its

contribution to the static loading protocol is important to note.

While this study featured unique calibration and loading protocols, the conditioning methods

used were those recommended by manufacturers. Conditioning, or exercising, is the process of

cyclically loading and unloading the sensor before calibration and actual testing. Several other

studies have used the same conditioning method, as it has become the standard in the field [31],

[39], [64]. Tekscan, the manufacturer of common FlexiForce ® sensors, states that the process of

conditioning helps to lessen the effects of drift and hysteresis [44], however there is limited data

validating such conditioning methods [79]. In a study performed by Hall et al., the sensitivity to

shear loading was eliminated by extended sensor preloading in compression and shear [79]. In

the same study, they were able to compensate for hysteresis errors by including terms dependent

on loading history in the calibration equation [79]. Future work could examine the effects of

various conditioning methods on parameters including accuracy, hysteresis, and drift.

Future work examining dynamic loading should examine various force/pressure ranges to

simulate the pressure distributions at a range of locations within a body-device interface, as well

69

as a range of activities. For example, the forces at both peaks and valleys can be customized, as

well as the loading rates.

It is important to acknowledge the assumption of uniformity in tissue loading present in this

paper. The testing performed featured a uniform layer of silicone simulating tissue at the body-

device interface. In true applications, the unique anatomy of any limb (i.e., presence of bony

prominences) presents many inconsistencies in tissue properties (e.g., varying compliance,

curvature, etc.). While the presence of the thin rigid backing did not have a significant effect in

this work, this would not likely hold true with nonuniform tissue. Future studies could examine

the effects of nonuniformity in tissue on the sensor’s performance.

Additionally, this study focused on the evaluation of single point FSRs. While the information

regarding the pressure at a single point is valued, it is more valuable to map the pressure

distributions over the entire residual limb during gait [48]. Upon validation of the QTC prototype

sensor, future work should examine creation of sensing arrays.

While the performance of the QTC prototype sensor did not quite meet commercial sensor

standards, the sensor showed promising results for an initial sensing prototype. Future iterations

should focus on thinning the QTC sensing layer (i.e. screen printing), as this may improve sensor

response time, as well as improve the integrability within systems. Future work will also be

required to determine and refine sensor electrical integration properties, as the thin copper layer

is appropriate for bench top testing purposes only. Additionally, future work on sensor

development should study the sensor response time and drift effects under clinically relevant

conditions.

Upon completion of benchtop testing validating the sensor’s performance, a clinical pilot study

will be conducted. This will consist of the integration of the sensor into an assistive device,

creating an interface with embedded sensors. A conventional lower-limb prosthetic socket or

orthosis will be modified, and a small clinical pilot study will be conducted, comparing the QTC

prototype sensor with a gold-standard sensor in pressure measurement (e.g., commercial strain

gauge).

70

5.2 Significance

In this work, novel approaches to evaluating force sensor performance at clinically relevant

body-device interfaces were developed and applied. This work includes the characterization of

existing transducers as well as the development of a novel sensor for assistive device

applications. Beyond the scope of this thesis, the overarching goal of this work was to advance

the use of interfacial pressure sensors at the body-device interface to facilitate quantification of

fit of MAT devices, and improve comfort, function, and satisfaction for users. The development

of a novel testing protocol simulating clinically relevant body-device interfaces has the potential

to disrupt current sensor evaluation methods. The novelty of the protocol arose from the dynamic

gait simulation, as well as the study of the effects of area of applied load and sensor

configuration. This method can be used to better understand sensing at the body-device interface,

and aid in developing new sensing technologies.

The evaluation of the QTC prototype sensor will act as a proof-of-concept for further

development of the technology, and 3D-printed assistive devices with embedded sensors. Fully

integrating sensors for measuring biomechanical and physiological aspects of prosthetic and

orthotic function is an essential first step towards fully adaptable intelligent prostheses that can

both detect and respond to the user’s changing physiology, anatomy, functional requirements, as

well as activities and environment.

71

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force,” J. Biomech., vol. 41, no. 16, pp. 3492–3495, Dec. 2008.

77

Appendices

Appendix A: Explaining the Deadband in Hysteresis Data

Figure A-1: Raw data and fitted curve for calibration data showing no collected data below 3.5 N.

Figure A-2: Raw resistance, calculated force, and applied force waveforms displaying deadband.

Applied Force

Calculated Force

Raw Resistance

78

Appendix B: QTC Prototype Sensor Inter-Sample Repeatability

To evaluate the inter-sample repeatability, a force-sweep from 0-10N was applied to two samples

of the QTC prototype sensor using the 15 mm diameter load tip attachment. No backing or puck

were used. Five trials were repeated per sample. Figure A-3 displays the force-resistance curves

for the five samples for each sample.

Figure A-3: Characteristic resistance vs. force curve for two QTC prototype sensor samples.

The coefficient of variation was calculated across the five trials of each sensor sample, as well as

across all 10 trials of both sensors to quantify the repeatability. While each sensor’s repeatability

was approximately 10% (9.3 and 10.2% for sensors 1 and 2, respectively), the inter-sample

repeatability was 20.5%. This value surpasses the 10% goal, indicating future work is required in

refining future iterations of the sensing prototype.

79

Appendix C: Additional Data

Table A-1: CV values calculated for each material, configuration and area for both Peratech and

Sensitronics sensors, using both matched-area and generalized-area calibration methods.

Material Config Area

Coefficient of Variation (%)

Peratech

Matched-Area

Peratech

Generalized-

Area

Sensitronics

Matched-Area

Sensitronics

Generalized-

Area

Extra Soft NBNP 05 1.3 15.1 11.5 14.2

08 0.8 0.8 7.7 7.7

15 5.6 38.1 13.7 53.5

25 3.9 55.0 8.9 76.3

NBYP 05 0.5 2.6 14.3 20.9

08 0.7 0.7 9.4 9.4

15 0.9 1.4 13.2 19.7

25 0.9 2.4 7.9 12.2

YBNP 05 1.3 14.9 10.3 16.6

08 0.7 0.7 11.3 11.3

15 6.0 49.6 22.2 69.7

25 2.4 44.3 6.8 50.4

YBYP 05 1.2 5.9 6.7 15.1

08 1.4 1.4 7.8 7.8

15 0.7 2.8 7.4 10.3

25 0.7 2.1 10.4 10.0

Soft 10A NBNP 05 0.6 12.2 7.1 13.2

08 0.8 0.8 6.7 6.7

15 17.5 42.6 9.2 81.5

25 16.9 46.7 6.9 66.8

NBYP 05 0.6 5.0 10.3 9.7

08 0.6 0.6 8.7 8.7

15 0.9 1.9 6.0 15.2

25 0.9 3.5 8.7 9.6

YBNP 05 0.9 16.7 12.6 15.7

08 1.0 1.0 9.5 9.5

15 8.1 47.7 8.4 72.7

25 13.6 59.3 14.1 45.6

YBYP 05 2.7 9.2 9.2 11.7

08 1.4 1.4 5.5 5.5

15 1.7 2.5 7.6 9.1

25 0.8 4.7 3.4 8.7

Soft 20A NBNP 05 0.6 12.0 4.9 16.4

08 0.9 0.9 3.6 3.6

15 21.2 39.0 8.8 31.8

25 6.1 49.9 26.9 22.4

NBYP 05 1.2 4.6 8.6 10.6

80

08 0.8 0.8 6.7 6.7

15 1.4 1.5 7.1 11.5

25 0.7 2.3 7.8 11.0

YBNP 05 0.9 14.3 6.4 14.4

08 0.9 0.9 4.7 4.7

15 12.1 63.9 6.2 25.6

25 5.6 43.4 29.3 21.1

YBYP 05 1.3 5.2 5.7 16.5

08 1.7 1.7 9.2 9.2

15 1.7 2.8 7.9 13.8

25 1.1 3.9 4.2 8.8

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