Galbraith Pater AW 201109 MSC

8/3/2019 Galbraith Pater AW 201109 MSC

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Development of a novel link-segment model

for estimating lower back loading in paramedics

by

Peter Alexander Wetherall Galbraith

A thesis submitted to the School of Kinesiology & Health Studies in

conformity with the requirements for the degree of Master of Science

Queens University

Kingston, Ontario, Canada

September 2011

CopyrightPeter Alexander Wetherall Galbraith, 2011

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Abstract

Work conducted as part of this thesis evaluated the lifting techniques

of paramedics using a novel link-segment model that was validated

against a commercially available software package, 3D Static Strength

Prediction Program (3DSSPP).

Site visits to four paramedic services across the province were

conducted to gain information about bags weights and lifting

techniques. Twenty-five paramedics then visited the Biomechanics Lab

at Queens University to participate in testing sessions mimicking the

daily lifting and carrying tasks performed by paramedics on the job.

Participants were outfitted with the Xsens Motion Tracking System and

asked to lift and carry bags ranging from 5-20kg. Output from the

Xsens system was used in a 3D-inverse dynamic model to estimate

loading at the L5/S1 joint. The compressive and shear force estimates

at this joint are of particular interest given their correlation with low

back pain and injury.

Across all conditions the greatest compressive forces were seen during

bag pickup and bag release. Additionally, reaching forward 50 cm at

pickup increased peak spinal compressive loads by nearly 300N and

500N for a 5kg and 10kg handbag respectively. Not surprisingly, at bag

release greater trunk lean values were correlated with higher

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compressive force estimates. Single-shoulder backpack carries showed

similar loading characteristics when compared to double-shoulder

backpack carries. Shear force estimates remained well below

acceptable levels across all conditions.

Based on paramedic feedback, a supplementary testing session was

performed with a single participant to evaluate multi-bag carries and

stair climbing. The results of this testing session showed that loading

was reduced at pickup and release when the load was distributed

across two bags.

This research led to the development of four recommendations that

have been presented to the Association of Municipal Emergency

Medical Services of Ontario.

1. Paramedics should not lift single bags or a combination of bags

that exceed 20kg.

2. Prior to lifting, bags should be located as close to the

paramedic as possible.

3. When placing bags on the ground and when picking bags up

off of the ground, paramedics should use a squat lift technique

to prevent forward and side bending.

4. When multiple bags are carried the load should be evenly

distributed within bags and across sides of the body.

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Acknowledgements

I would like to start by thanking my supervisor Dr. Pat Costigan for his

help and guidance from start to finish. Not to mention saying that an

independent study on longboarding was entirely reasonable.

Dr. Joan Stevenson, without whom this project could not have

happened.

Thank you to my family (Mom, Dad, and Jamie, as well as my extended

family in Kingston and Calgary) for encouraging a healthy curiosity in

all things and supporting me throughout my life.

Rachel for being there at the end of some long days and always being

willing to discuss my research.

The many friends I have made in the department for always being

ready for a celebratory or conciliatory pint, and providing me with so

many lessons and experiences that could never take place in a

classroom.

Table of Contents

TOC \o "1-3" Abstract

Table 1 - Conditions presented to paramedics during in-lab testing. Allconditions included a 4m carry.

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Figure 1 - XSens Motion Tracking System sensors. Sensors are placedon the lower arms (1&2), upper arms (3&4), scapulae (5&6), upperback (7) and lower back (8).

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Chapter 1: Introduction

In August 2010, the Association of Municipal and Emergency Medical

Services (AMEMSO) contacted the Queens University Biomechanics

Lab in the School of Kinesiology and Health Studies to investigate the

weight and design of equipment bags carried by paramedics in the

province of Ontario.

The 72 certified land ambulance services in Ontario respond to an

estimated 1.5 million calls annually. In responding to these calls,

paramedics carry their equipment bags over long distances, up and

down stairs, and through confined spaces. Given that paramedics can

respond to a number of calls per shift, it is not surprising that the

various types, size, and weights of these bags are a concern. Despite

the fact that common equipment is carried in the bags, there are no

standards governing the size and number of bags or the weight carried

in any particular bag.

The results of this research provides insight into the forces

experienced by paramedics while lifting equipment bags on the job

and details the aspects of lifting that increase the risk of injury as put

forward by the National Institute for Occupational Health and Safety

guidelines (Waters et al. 1993). From these findings, the researchers

recommend ways to improve the lifting conditions and reduce the

strain on paramedics while lifting and carrying bags on the job.

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For paramedics, two common scenarios occur: (1) when they are

required to lift an individual from the ground while holding a bag in one

hand, and (2) when they have to guide themselves and the patient

around or over obstructions. The variety of sites that paramedics visit

means that paramedics often experience awkward postures. Studies

assessing paramedic injuries have shown that low back strains are a

major source of time off work and may be one reason why paramedics

have such high injury rates (Hogya & Ellis 1990; Okada et al. 2005)

Clearly, it is important to understand the loads experienced by

paramedics across a variety of lifting conditions. An understanding of

the magnitude of these loads while performing paramedic work is

essential to determine if the weights and lifting techniques are safe.

Biomechanical measures that have been correlated with increased risk

of low back pain include: peak compressive force, peak shear force and

the cumulative load experienced by the L4/L5 or L5/S1 joint, (Norman

et al. 1998; van Dien & Toussaint 1997). Compressive forces act

along the craniocaudal axis of the spine and under normal loads the

vertebral body withstands most compressive forces. However, the

extreme case can lead to disc herniation or prolapse (Roaf 1960).

NIOSH guidelines (NIOSH, 1981) have put forward a maximum

acceptable limit of 3400N and maximum permissible limit of 6400N of

compressive force. The acceptable limit represents a compressive

force value that should be safe for 75% of women and 99% of men.

Beyond 6400N higher risk for injury is predicted. Shear forces act in

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the medial-lateral and anterioposterior directions along the spine and

are another risk factor for low back pain (Kerr et al. 2001). McGill et. al.

(1998) have put forward a maximum acceptable limit of 500N and a

maximum permissible limit of 100N for shear force along the

anterioposterior axis of the spine. Traumatic events are not always the

cause of low back pain. Repetitive loading of even small amounts can

lead to low back pain and injury over time. If we consider each of these

loading instances a single trauma, the cumulative loading is the

accumulation of small traumas over time. As no cumulative exposure

limits have been found in the literature, determination of safe and

unsafe tasks based on cumulative loading is difficult.

Link-segment modeling is often used to quantify these compressive,

shear and cumulative loads of the task at hand. Link segment models

(LSM) use basic physics equations and represent the body as a series

of rigid, connected segments. LSM incorporate anthropometrics and

individual measurements of motion as well as estimates of the external

forces to provide an estimate of the forces and moments imposed on

the joints. Most LSM of the spine also estimate the surrounding

musculatures force contribution that is often substantial and,

therefore, must be considered. The compressive force contributed by

the back extensors, which includes many different muscles, is often

estimated by representing all muscles as a single muscle equivalent.

Joint load estimates increase when dynamic parameters of motion are

included and when the lifting task is asymmetrical (Marras & Granata

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1995). For better estimates of joint load, it is important to use a fully

dynamic three-dimensional model.

We hypothesized that heavier loads would lead to higher compressive

force estimates and that lifts using backpacks and shoulder bags would

produce lower estimates than those conditions using handbags

because of the improved load location. Additionally, it was expected

that those paramedics that adopted a squat posture when releasing

bags would experience lower compressive forces than those who chose

to lean forward to release the bag. It was hoped that this study would

enhance the bag selection and design criteria as well as producing

guidelines for appropriate lifting technique for paramedics.

At the conclusion of this research, the results were presented to

AMEMSO in the hopes of improving bag weighting and lifting policies

across the province.

Chapter 2: Review of Literature

2.1 Review of Modelling Literature

Human link-segment models have been used in video games,

rehabilitation, and biomechanical settings to record and gain

understanding about human motion. The human body is modeled as a

series of connected rigid links based on subject-specific

anthropometric parameters. Link-segment models are then used as

input to inverse models using basic force and moment equations to

quantify the forces and moments experienced by the spine, especially

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when examining lifting (Kingma et al. 2001; Abdoli-Eramaki et al. 2009;

Norman et al. 1998; Potvin, McGill, & Norman 1991). To better

estimate loading on the lower back the muscular contributions must be

taken into account. To include the inertial components required in a

dynamic inverse model, effective motion capture tools are required to

measure the motion under investigation.

2.1.1Capturing Human Motion

Before any investigation can be made into the kinetic or kinematic

properties of human motion, researchers must be confident that the

recorded motions closely represent the actual motions that took place

during data collection. Early motion capture techniques relied heavily

on film recordings and are time consuming to process, often requiring

manual digitization and error checking throughout the entire process.

The advent of digital video recordings has sped up many of these

processes and is still a key component of modern biomechanical tools

such as HU-M-AN (HMA Technology, Canada), 3DSSPP, (University

of Michigan, USA) and 3DMatch (University of Waterloo, Canada).

Other motion capture systems require participants to be instrumented

with light emitting diodes or reflective surfaces. These motion capture

systems, such as Vicon Nexus (Vicon Motion Systems, USA) and

Optotrak Certus (Northern Digital Inc, Canada), require line of sight

and multiple cameras to automatically record 3-dimensional

movement, and boast sub-millimeter accuracy. These systems can

have large capture volumes but are not particularly portable. Local

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coordinate systems are created using the sensors attached to each

segment. The known orientation between relevant anatomic landmarks

and local coordinate system allows the creation of an anatomical

coordinate system for each segment. Link-segment models are then

built from the anatomical coordinate systems and known

anthropometrics. As each segment is located in a global reference

frame regardless of the orientation and position of other segments,

measurement errors remain relatively constant across all segments.

A relatively new technology has emerged in the past decade allowing

researchers to capture motion in the field without the need for line of

sight or large systems. Such systems rely on accelerometers,

gyroposcopes, and magnetometers and advanced software to produce

reliable estimates of body segment position and orientation

(Roetenberg, Luinge, & Slycke 2009). The Xsens Motion Tracking

System (Xsens, The Netherlands) is one of these systems and has been

used in motion capture labs and for video game and movie motion

capture. These systems are highly valuable for the commercial setting

given their real-time capabilities and ease of use. However, the

scientific community has questioned the accuracy of these systems for

scientific research (Cutti et al. 2006; Luinge & Veltink 2005; Damgrave

& Lutters 2009; Brodie, Walmsley, & Page 2008). The main problem is

that the measurements drift due to the reliance on magnetometers to

determine the sensors heading. Aligning each sensors local

coordinate system with the supposed underlying anatomical

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coordinate system approximates anatomical coordinate systems. Link-

segment models developed using inertial sensors must be built

sequentially from proximal to distal (or vice-versa). In most cases, the

origin of the most proximal segment is assumed to exist at the origin

of the global coordinate system. The most proximal segment pivots

about the origin based on the orientation of the attached sensor. The

most proximal segments endpoint is used as the next segments start

point and the process is repeated. Thus errors are accumulated as the

LSM is built from proximal to distal, and the greatest position errors are

seen in the distal segment. Image-based motion capture systems such

as Vicon Nexus are able to avoid cumulative errors in segment

orientation because they locate each segment in a global reference

frame.

The effect of these errors on kinetic and kinematic parameters has

been evaluated. Godwin (2009) compared a link-segment model built

using Xsens sensors to one using Vicon Nexus and found distal

segment endpoint RMS errors of 136 mm, 138 mm, and 101 mm in the

anterior-posterior(AP), medial-lateral(ML), and inferior-superior(IS) axes

respectively. These errors led to flexion moment RMS errors of 12 Nm,

10Nm, and 4Nm along the AP, ML, and IS axes respectively. These

values may seem small but represented between 10% and 30% of

peak moments across a variety of trials.

Since Godwins work, improvements have been made to the Xsens

system by improving the filtering of the accelerometers and

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gyroscopes. In 2005, Zhou, Hu & Tao showed average error for distal

endpoint locations ranging from 10-70mm, and in 2010, Zhou and Hu

showed that the system now has RMS position errors of 9mm, and

drifts of less than 5 mm/s when used while performing daily

activities.These improvements appear to be made by improvements to

the proprietary Kalman filter; a technique that combines the predicted

and observed values for the accelerometers, gyroscopes and

magnetometers. For an explanation of this process see Brodie,

Walmsley & Page (2008).

These improvements in Xsens output have led to improvements in

kinematic accuracy. Cutti et al. (2010) reported RMS joint angle errors

of 1.4 and 1.8 for the hip and knee angles when compared with a

standard goniometer and errors of approximately 2 at the hip and

knee. Basic drawing tests* have shown the Xsens system to be

accurate within 0.5 cm using kinematic modeling over periods of 25

seconds and has been deemed acceptable in a neurorehabilitiation

setting (Bai et al. 2011). When investigating gait parameters similar

repeatability measure values were found when comparing the Xsens

system (Cloete & Scheffer 2010) to Vicon Nexus (Kadaba et al 1989)

and Polhemus Liberty (Mills et al 2007).

*These drawing tests required participants to repeatedly trace a triangle. Thus when the link-segment

model tightly matches the dimensions of the triangle over a series of repetitions we assume that the

model is valid.

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2.1.2Modelling the Shoulder Joint

Godwin (2009) notes one limitation of her model was that the

glenohumeral joint is unable to translate relative to the spine.

However, there can be as much as 150 mm of protraction (anterior

translation) of the glenohumeral joint itself (Albert et al. 1998),

suggesting that a rigid connection is invalid. As noted by Godwin, the

inability to represent shoulder protraction may account for some of the

errors in distal segment endpoint location.

Godwin (2009) and others simply assume that the shoulder joint

maintains a constant orientation and position relative to the upper

body segment (Cutti et al 2008; Rau, Disselhorst-Klug, & Schmidt

2000; Rab, Petuskey, & Bagley 2002). In these cases the glenohumeral

joint is assumed to act as a hinge joint with no shoulder translation.

Godwins model is an example of an open loop system where each

segment is linked to only one other segment in a chain from back

segment to hand segment. Other models (van der Helm 1994a,

Dickerson, Chaffin, & Hughes 2007; Maurel et. al. 2010) have

constrained shoulder joint translation using a closed link between the

scapula, clavicle, and upper back. In this way a triangle is formed

between the clavicle, scapula and back and thus shoulder joint

translation is limited but not rigid. Yang et al (2010) have suggested

that closed-loop models have higher accuracy and fidelity than open-

loop models.

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Three bones make up the shoulder complex: the clavicle, humerus and

scapula. The only linkage between the axial skeleton and the upper

extremity occurs where the clavicle attaches to the sternum to form

the sternoclavicular joint. Sternoclavicular range of motion has been

estimated at 20 degrees of protraction (elevation), 60 degrees of

forward flexion, and 10 degrees of axial rotation (Inman, Saunders &

Abbott 1944; van der Helm 1994b; van der Helm & Pronk 1995). The

glenoral fossa of the scapula and the head of the humerus form the

glenohumeral joint that permits movement of the upper arm. To

represent motions such as shoulder shrugs, and pinching the shoulder

blades, LSMs built using inertial systems must instrument the clavicle

or scapula to gauge shoulder joint translation.

Recommendations exist about how best to model the shoulder

complex (Wu et al. 2006); however, problems arise when trying to

securely and comfortably attach a sensor over the clavicle (Cutti et al

2008). Additionally, the shoulder joint centre is difficult to determine

because it is located deep below the skins surface hiding the bony

landmarks that are necessary for joint centre determination (Rau,

Disselhorst-Klug, & Schmidt 2000).

The effect of shoulder joint translations on inverse dynamic model

output should be considered to see if differences between shoulder

models lead to substantial differences in force estimates. The only

research we found that attempted to quantify this effect showed that

shoulder translation had a significant effect on positions and

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accelerations of arm segments, but did not have a statistically

significant effect on L5/S1 moment prediction (Albert et al. 1998). Rab

et al. (2000) argued that shoulder joint centre determination errors of

20mm had a negligible effect on shoulder kinematics. It may be the

case that shoulder joint centre differences have a small effect on

kinetic and kinematic results.

2.1.3Kinetic Parameters

Once link segment models have been built, researchers can

investigate the kinetic or kinematic parameters of the model.

Kinematic parameters such as: joint angle, segment angle, range of

motion, displacement and velocity can be used to investigate

differences between groups of individuals or attempt to describe

specific aspects of human motion. Alternatively, kinetic parameters

can be investigated to gain an understanding of the forces and

moment to which the body is subjected. Newtonian physics, built upon

the fundamental equations of motion of a rigid body (Zatsiorsky 2002)

are used to determine the mechanical forces on various body

segments. These parameters are often broken down into their

component vectors and expressed in the local coordinate system of

the segment of interest. For example force vectors at the L5/S1 disc

are often broken down into: a compressive force acting cranio-

caudally, and two shear forces acting anterior-posteriorly and medial-

laterally.

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2.1.4 Estimating Muscular Contribution

Calculations based on equations of motion do not provide a true sense

of the loading at specific joints. To produce better estimates of joint

loading, many models include the force contribution made by the

surrounding musculature. As many as 104 different components have

been incorporated into spinal models to balance the forward flexion

moment using passive and active tissues (Callaghan & McGill 2001),

while others use a single back extensor model (Norman et al. 1998).

Mathematically -driven optimization models (Brown & Potvin 2005)

have also been used to estimate spinal loading.

Complex electromyography assisted models (Gagnon et al. 2011;

Mientjes et al. 1999; Davis, Marras, & Waters 1998) include the

activation of dozens of muscles to estimate loading on the lower back.

Potvin et al. (1991) recorded EMG activation of 11 different muscles

during symmetrical squat and stoop lifts. They partitioned the reaction

moment into 11 muscles and 7 ligaments based on each muscles

activation and each ligaments strain based on lumbar flexion angle.

Partitioning the reaction moment depended upon assumptions of:

ligament stress-strain curves, muscle lines of actions, muscle cross-

sectional area, and the modeled relationship between muscle

contraction velocity, activation and produce muscular force. A large

number of assumptions must be made to produce EMG-assisted

models.

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Given the number of calculations and assumptions required to build

EMG-assisted models some researchers have elected to avoid EMG

altogether. Optimization models are mathematically-driven functions

that partition the reaction moment based on assumed muscular

activation patterns. In essence, these models assume the central

nervous system is acting to achieve biomechanical equilibrium by

minimizing some objective function such as muscular contraction (An

et al. 1984, Crowninshield & Brand 1981; Nussbaum, Chaffin, &

Rechtien 1995), joint force (Brown & Potvin 2005), metabolic energy

consumption (Davy & Audu 1987), or some combination of these

factors (Bean, Chaffin, & Schultz 1998; Pel et al. 2008; Seireg & Arvikar

1973). Again, a large number of assumptions are required. One

problem with optimization models is their inability to accurately predict

muscular co-contraction, which may lead to underestimates of joint

loading (Cholewicki, McGill, & Norman 1995).

An alternative to optimization models, while still avoiding EMG, is a

single equivalent muscle extensor model, in which all muscles that

assist in flexion are assumed to act as a single muscle group. This

model assumes a single extensor muscle group is the sole means by

which the body counterbalances the forward flexion moment, and acts

exclusively about the flexion axis. It should be noted that because this

muscle group is modeled as acting along the long axis of the spine it

cannot counterbalance moments about the inferior-superior (IS) or

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anterior-posterior (AP) axes of the spine. However, it does produce a

force vector that increases compressive estimates.

Decisions regarding moment arm length are important in this case as

small length differences can lead to large variations in final joint

loading estimates. McGill & Norman (1987) state that 5 cm is a

commonly used value for the single extensor muscle moment arm,

while Norman et al. (1998) have used a moment arm length of 6cm.

These values are in keeping with the real moment arm length for the

erector spinae muscle group range of 4.9-6.4 cm (Jorgensen et al.

2001). To factor in some muscular effort to balance IS and AP shear

forces, varying the line of action within realistic ranges has been

suggested (van Dien & de Looze 1999). These variations can change

compressive force estimates by more than 100N and shear force

estimates by more than 50N (Nussbaum, Chaffin, & Rechtien 1995).

Varied lines of action may not substantially change model output given

that heavy lifts commonly exceed the action limit of 3400N of

compressive force (NIOSH 1981). These variations may be more

relevant for shear force output given that the maximum acceptable

limit for shear force is 500N (McGill 1998).

It is important to understand how different muscle models influence

force output estimates. Potvin et al. (1991) showed that for a 32-kg lift

at peak lumbar spine flexion the erector spinae contributed 74%

(stoop) and 83% (squat) of all compressive force contributions made

by muscular or ligamentous tissues. This indicates that in sagittal

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plane lifting the erector spinae are the primary contributors to

muscular compressive force, and a single extensor muscle model may

be an appropriate method in this instance. Cholewicki, McGill, &

Norman (1995) found that optimization models predicted

approximately 30% lower L4/L5 compressive force values than EMG-

assisted models, possibly because the optimization model cannot

accurately predict co-contraction of antagonistic muscle pairs.

There are inherent problems to each model when estimating muscular

force contributions. Single muscle extensor models are simplistic and

do not attempt to represent the true nature of the back musculature

while specifically lacking the muscles acting at oblique angles to the

spine that stabilize the spine during twisting and asymmetrical

motions. EMG-assisted models rely on a length-strength and force-

activation relationship for each muscle investigated and these

relationships may be flawed, especially when considering different

strength capabilities across individuals. Additionally, while stability is

visibly maintained during most lifts performed during data collections,

Brown & Potvin (2005) noted that EMG-assisted models may produce

situations where equilibrium is not maintained. As noted before,

optimization models rely on mathematical solutions to determine the

force contributions of the musculature in the back, and often do not

include agonist-antagonist muscular co-activation. In each case some

drawbacks are accepted with the goal of achieving more realistic

loading estimates. Despite (and possibly because of) their simplicity,

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single equivalent extensor models continue to be used in

biomechanical research.

2.2 Review of Lifting Literature

Lifting is a risk factor for low back pain (Marras et al. 1995; Chaffin &

Park 1973). The 2009 U.S. Department of Labors Annual Survey of

Occupational Injuries found that 46% of musculo-skeletal disorders

were associated with the back and required on average 7 days off

work. This survey also found 116,530 injuries requiring time off work

were the result of overexertion while lifting. Studies have examined

psychosocial, physiological and biomechanical criteria to determine

risk factors for low back pain. These factors include: previous instances

of low back pain, job satisfaction, job stress, repetitive lifting, heavy

lifting, forward flexion, axial twisting, overhead reaching and hand

coupling to name a few (Hoogendoorn et al. 2000; Kerr et al. 2001;

Marras et al. 1995, 1999; Woolf & Pfleger 2003; Frymoyer et al. 1983;

Brinckmann et al. 1998). Jobs requiring frequent and heavy lifting have

been associated with increased risk of disc herniation and low back

pain in general (Kelsey et al., 1984, Kelsey & White, 1980). Attempts to

limit low back injuries have focused on improving postures while lifting

and reducing loads (NIOSH 1981; Waters et al 1993).

2.2.1Compressive Force Guidelines

Compressive forces act along the IS axis of the spine and under normal

conditions the vertebral body withstands most compressive forces.

However, the extreme case can lead to disc herniation or prolapse

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(Roaf 1960). Cadaveric research has shown that ultimate compressive

forces may been in the range of 2100- 9600N (Brinckmann et al.

1988), having a mean of 4400N with a standard deviation of 1880N

(Jger & Luttman 1989).

In 1993, the National Institute for Occupational Safety and Health

released their lifting equation that identified hazardous lifting tasks

based on biomechanical, psychosocial, and physiological factors. Two

compressive limits are put forward: the action limit (AL) of 3400N, and

maximum permissible limit (MPL) of 6400N. The biomechanical

criterion for the equation is based on research showing that: spinal

compressive forces of greater than 3400N may increase the risk for

low-back injury and injuries may become quite likely beyond 6400N.

Waters et al (1993) argue that if the data were normally distributed

21%-30% of lumbar segments would fail when loaded with a force of

3400N given the ultimate compressive force values put forward by

Brinckmann et al (1988) and Jger & Luttman (1989). Interestingly,

Norman et al (1998) found that the mean peak compressive force of

individuals reporting low back pain was 3423N. The AL of 3400N is a

conservative estimate for a healthy working population since cadaver

lumbar segments may have lower tolerance limits because of declines

in lumbar strength with age, as well as changes in bone mineral

content (Hansson & Roos 1981). However, when ensuring workplace

safety, conservative limits should be used.

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2.2.2 Shear Force Guidelines

Shear forces act in the medial-lateral and anterioposterior directions

along the spine and are a risk factor for low back pain (Kerr et al.

2001). Krypton et al. (1995) found shear force tolerance limits in

cadavers of between 1700N and 2900N. While less work has been

done in this area, some guidelines have been developed based on

thinking similar to that of the NIOSH Equation. McGill et al. (1998) has

put forward an action limit of 500N and a maximum permissible limit of

1000N; these limits are akin to the 3400N action limit and 6400N

permissible limit for compressive force. A shear force limit of 500N has

been used with reasonable success to predict which workers reported

low back pain (Daynard et al. 2001). Tasks that keep compressive

forces below 3400N and shear forces below 500N are unlikely to

increase the risk of injuries.

2.2.3Model Complexity

Another important point to consider in developing a link segment

model is whether to include dynamic components in force and moment

calculations. Static models are easier to implement, as fewer

calculations are required. Static and quasi-static models have been

developed and applied successfully for many years particularly for

slower motions, but in cases where inertial contributions are non-

negligible, dynamic models may be more accurate in predicting

loading due to the inertial effects of the load and body segments

(Marras & Granata 1995; McGill & Norman 1985; Lindbeck & Arborelius

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1991; de Looze et al. 1994). Substantially higher predicted loads have

been seen when acceleration components are included in force

calculations when compared to what is otherwise the same task (Jger

& Luttman 1989).

Asymmetric lifting is a common occurrence for most tasks due to

differences between load origin and destination, movement

requirements, obstructions or a variety of other reasons. To that end,

postural symmetry is unlikely to happen in working environments. In

spite of this many models only take 2D motions into account when

analyzing lifting (Albert et al. 1998; Anderson et al. 1985; Waters &

Garg 2010). Underestimations of the peak torque have been shown as

high as 60% when loads are placed at 90 to the sagittal plane

(Kingma et al 1998). Lift asymmetry is identified in the NIOSH lifting

equation as a factor that reduces the maximum load that an individual

can safely carry (Waters et. al., 1993) It is suggested that researchers

may arrive at the wrong conclusions when a 2D model is used for tasks

with 30 or more of twisting (Kingma et al. 1998). For these reasons

three-dimensional dynamic models should be used when possible.

2.2.4Paramedic Injury Rates & Lifting Demands

Paramedics share similar lifting task demands with a variety of

professions such as nurses, nursing aides, and fire-fighters who are

required to support a patient and administer some level of care while

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simultaneously transporting the tools necessary to provide that care.

Studies of the nursing population have shown high levels of low-back

pain much of which is related to patient handling, (Jensen 1987,

Videman 1984) a task that paramedics are frequently required to

perform, with potentially greater strain due to awkward postures and

lower lift origins. The compressive force that the spine is subject to

while transferring a patient from one space to another has been shown

to exceed even the NIOSH maximum permissible limit of 6400N

(Marras et al. 1999). It is believed that workers cannot tolerate

compressive forces beyond this limit without increasing their risk for

injury (Waters et. al. 1993). Patient handling tasks expose paramedics

to potentially dangerous low back loads. These loads may be

contributing to the high back injury rates seen in paramedics (Crill &

Hostler 2005).

Back strain injuries (as classified by the International Classification of

Diseases, adapted, 8th revision (ICDA 1967)) are very common in the

paramedic population and account for 36% of all injuries; of those

more than half are caused by lifting activities (Hogya & Ellis 1990).

This same study found that each instance of reported back strain led

to, on average, 4 days of time off work. High paramedic injury rates

are not just a North American phenomenon. A survey of Japanese

paramedics and emergency medical technicians found that 25% of

respondents had experienced a low back problem in the previous 12

months, and 1/5th had experienced pain for 30 days or more in the

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same time span (Okada et al 2005). Patient handling may not be the

only contributor to injury. Spending as little as 10% of working time in

30 of trunk flexion has been shown to increase the risk for developing

low back pain (Hoogendoorn et al. 2000). Paramedics are often

required to bend over to lift and interact with the patient and when

administering care in the back of the ambulance may spend much of

their time in forward flexion. Due to the varied and demanding nature

of the job it seems inevitable that paramedics are exposed to those

factors that lead to low back pain. Work must be done to improve the

working environment for paramedics to prevent injuries and time off

work.

Chapter 3: Creating the Link-Segment Model

3.1 Introduction

The goal of this research was to understand the loads generated by

individual bag lifts performed by paramedics. Link-segment modeling

was used to meet this goal; in this case a novel three-dimensional

dynamic hands-down model was developed that incorporated the

output from a system of Xsens motion trackers.

3.2 Review

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Human link-segment models have been used in video games,

rehabilitation, and biomechanical settings to record and gain

understanding about human motion. Link-segment models are then

used as input to inverse models using basic physics equations to

quantify the forces and moments experienced by the spine, especially

when examining lifting (Kingma et al. 2001; Abdoli-Eramaki et al. 2009;

Norman et al. 1998; Potvin, McGill, & Norman 1991). To include the

inertial components required in a dynamic inverse model, effective

motion capture tools are required to measure the motion under

investigation. In order to better estimate loading on the lower back the

muscular contributions must be taken into account.

A relatively new technology has emerged allowing researchers to

capture motion in the field without the need for line of sight or bulky

systems. Systems, like the Xsens Motion Tracking System (Xsens, The

Netherlands), rely on accelerometers, gyroposcopes, and

magnetometers and advanced algorithms to produce reliable

estimates of body segment position and orientation (Roetenberg,

Luinge, & Slycke 2009). Body segment positions, and accelerations can

be used as input for inverse models to determine kinetic properties

such as the net force and moment. Link-segment models developed

using inertial sensors must be built sequentially from proximal to distal

(or vice-versa). Thus errors are accumulated as the LSM is built from

proximal to distal, and the greatest position errors are seen in the

distal segment. The effect of these errors on kinetic and kinematic

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parameters has been evaluated. Godwin (2009) found wrist position

RMS errors of 136 mm, 138 mm, and 101 mm in the anterior-

posterior(AP), medial-lateral(ML), and inferior-superior(IS) axes

respectively, leading to moment RMS errors of 12%, 17%, and 22%,

about the AP, ML, and IS axes respectively, as a percentage of peak

moment.

Recent improvements in Xsens output have led to improvements in

kinematic accuracy. Cutti et al. (2010) reported RMS joint angle errors

of 1.4 and 1.8 for the hip and knee angles when compared with a

standard goniometer and errors of approximately 2 at the hip and

knee. Basic drawing tests, requiring subjects to repeatedly trace the

same pattern and then checking for fidelity,have shown the Xsens

system to be accurate within 0.5 cm using link-segment modeling over

periods of 25 seconds and has been deemed acceptable in a

neurorehabilitiation setting (Bai et al. 2011). When investigating gait

parameters similar repeatability measure values were found when

comparing the Xsens system (Cloete & Scheffer 2010) to Vicon

Nexus (Kadaba et al 1989) and Polhemus Liberty (Mills et al 2007).

Godwin (2009) notes one limitation of their model was that the

glenohumeral joint was modeled as a rigid link between the distal end

of the humerus and the spine . However, there can be as much as

150 mm of glenohumeral joint protraction (Albert et al. 1998),

suggesting that a rigid connection is invalid . The inability of the

shoulder to protract may account for some of the errors put forth by

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Godwin. Thoracoclavicular range of motion has been estimated at 20

degrees of protraction (elevation), 60 degrees of forward flexion, and

10 degrees of axial rotation (Inman, Saunders & Abbott 1944; van der

Helm 1994b; van der Helm & Pronk 1995). Because the clavicle is

challenging to instrument, some models simply assume that the

shoulder joint maintains a constant orientation and position relative to

the upper body segment (Godwin 2009; Cutti et al 2008; Rau,

Disselhorst-Klug, & Schmidt 2000; Rab, Petuskey, & Bagley 2002).

Thus, the humerus rotates about the shoulder joint centre while the

shoulder joint centre does not move at all. Other models have sought

to monitor clavicular or scapular motion to track shoulder joint

translation (Dickerson, Chaffin, & Hughes 2007; Maurel et al 2010; van

der Helm 1994a). Yang et al (2010) have suggested that maintaining a

closed loop between scapula, clavicle, and back segments is important

to improve accuracy and fidelity in shoulder models.

The only research we discovered that attempted to quantify this effect

showed that shoulder translation had a significant effect on positions

and accelerations of arm segments, but did not have a statistically

significant effect on L5/S1 moment prediction (Albert et al. 1998).

Once link segment models have been built, researchers can

investigate kinetic or kinematic parameters of the model. Newtonian

physics, built upon the fundamental equations of motion of a rigid body

(Zatsiorsky 2002) are used to determine the mechanical forces on

various body segments. There have been reported differences in joint

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load estimates based on the inclusion of dynamic parameters and

asymmetrical lifting (Marras & Granata 1995). The muscular force

contribution must also be taken into account to produce accurate low

back loading estimates. EMG-assisted models, optimization models and

single muscle extensor models can be used to estimate this force

contribution. Kinetic parameters are often broken down into their

component vectors and expressed in the local coordinate system of

the segment of interest. Two commonly investigated parameters are

the compressive and shear forces at the L5/S1 joint. For these reasons,

it is important to use a fully dynamic three-dimensional model

including some measure of muscular force contributions to accurately

estimate joint loading for these lifting tasks.

3.3 Link Segment Model

3.3.1 Subject Instrumentation

Subjects were outfitted with Xsens Motion Trackers (MTx) sensors on

the thoracic spine and lumbar spine, as well as the left and right

scapulae, forearm and lower arm (Figure 1). By aligning the MTx

sensors with the long axis of the segments the orientation of these

body segments can be tracked during lifting tasks. The segment

orientations are used to create a representation of the subject under

investigation.

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1 3

25 6

7

8

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Figure 1 - XSens Motion Tracking System sensors. Sensors areplaced on the lower arms (1&2), upper arms (3&4), scapulae (5&6),upper back (7) and lower back (8).

Each sensors orientation was recorded using MT Manager (Xsens,

Netherlands) and sampled at 100Hz. All data was exported using MT

Manager and imported into Matlab (R2009a, The MathWorks) to

create the link segment model. All subsequent data processing was

also performed in Matlab, except where noted.

3.3.2 Building the Link Segment Model

The LSM is built sequentially from the pelvis to the hands and is

designed to accurately represent the body segments. The orientation

of each individual segment is determined by the orientation of the

attached sensor, except in the case of the head and neck segment,

clavicle segment and spine to sternum projection segment (which are

discussed later).

Anthropometric measurements of each individual are taken to create

segments representing: L5-L1,T12-T1, C7 to ear canal, clavicle, upper

arm, and lower arm. Back segments are measured by palpating the

spinous processes and counting up and down from C7 (the spinous

process that protrudes the most in forward flexion). The clavicle is

measured from the top of the sternum to the acromion process on the

clavicle by palpation. The upper arm is measured from the lateral edge

of the acromion process to the lateral epicondyle of the humerus. The

lower arm is measured from the estimated joint centre of the elbow to

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the second knuckle. This helps creates a LSM that represents each

individual subject participating in the study.

The pelvis, the fixed and unmoving base of the model, is the lowest

part and as this is a hands-down model the lower limbs are not

included. Attached to the top of the pelvis segment is the lower back

segment. Its motion is based on the orientation of the lumbar sensor

and its length, like all segments in the model, is based on subject

specific measurements. The upper back segment is connected to the

top of the lower back segment with its motion based on the output of

the upper back sensor. The head and neck segment is assumed to

maintain the same orientation as the upper back segment and is

projected up from the upper back endpoint. From the top of the upper

back segment, a rigid segment is projected forward towards the

sternum and represents the subjects trunk depth at this point. This

virtual segment helps link the axial skeleton and the humerus.

Next, two segments are created, a virtual clavicle segment that runs

from the sternum to the shoulder joint and a scapula segment, based

on the orientation of the sensor positioned above the scapula, that

runs from the top of the upper trunk segment to the shoulder.

If we assume that the clavicle, with a fixed length, can pivot freely

about the sternum, it will describe a sphere of possible clavicle

endpoints. The orientation of the scapula sensor determines the line

that pierces the clavicle endpoint sphere. The point of intersection

between the line and the sphere is taken as the shoulder joint.

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Figure 2 - How the orientation of the scapula sensor is used todetermine the position of the clavicular endpoint.

Models that have a rigid link between the spine and the shoulder, as

has been done, cannot represent shoulder joint translation, as would

be seen in reaching forward or reaching overhead. Our model more

validly represents these motions. As this model can incorporate

asymmetric motions, this procedure is repeated for the other shoulder.

On each side of the body, upper arm segments are attached to the

shoulder (clavical segment endpoints) and then lower arms (forearms)

are attached in turn. The hands are assumed to be rigidly attached to

the forearms. All required segment lengths are taken from anatomical

measurements of each individual subject.

3.3.3 Model Optimization

This model is optimized in a two-step process. The first step concerns

the length of the virtual segment connecting the spine and the

sternum (spine-sternum length), which is assumed to be rigid. During

an optimization trial, the subject holds a solid object (a piece of wood)

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and moves their arms around in a series of dynamic pushes, pulls,

twists and swings, while maintaining a grip on the object. Thus, the

distance between the lower arm endpoints (hands) is maintained

throughout the optimization trial. The distance between the hands is

calculated for each frame and subtracted from the true distance (the

real length of the object). The root mean squared (RMS) error is then

calculated. The spine-sternum length is increased and then this

calculation is repeated. The spine-sternum length was increased from

0 cm to the measured chest depth of the subject (usually about 15-

23cm) in 1 cm increments. At the end of this procedure we have

calculated RMS errors for each spine-sternum length for a single trial.

The calculated distance between hands is shown for 6 different spine-

sternum lengths in Figure 3. It should be noted that this process

improves accuracy when the predicted values are far from the real

values, and improves precision when the predicted values vary around

the real values.

Figure 3 - The effect of different spine to sternum projection lengthson the calculated 3D distance between lower arm endpoints during atrial where hand-to-hand distance was maintained. The dashedblack line represents the true hand-to-hand distance.

For each spine-sternum length, the RMS difference for the entire trial is

calculated and the trial with the lowest RMS value is selected as the

optimized spine-sternum length. This change is then incorporated into

the model as this provides the greatest reduction in segment endpoint

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prediction errors. During a series of optimization trials, this length was

found to be between 12 and 16 cm, and is in keeping with those values

found by measuring that same distance in MRI images from the Visual

Human Dataset (US National Library of Medecine, 2011). The RMS

difference for each spine-sternum length is presented in Figure 4.

Figure 4 - Finding the spine to sternum projection distance thatminimizes error in lower arm endpoint distance calculations.

A second optimization was implemented because of the potential for

misalignment between the Xsens sensors and the anatomical

coordinate system of the segment to which it is attached. Again, the

goal was to reduce the RMS error of the distance between the hands

using the same optimization trial used to estimate the spine-sternum

length. Using these trials the clavicle segments are rotated in 3

increments from -30 to +30about their original Z (mediolateral) axis

and the RMS error of the 3D distance between hands is calculated for

each 3 increment. The segment is then rotated by 1/3 of the angle

with the lowest RMS value. For example if the lowest RMS error value is

found to exist when the segment is rotated 9, then the segment is

rotated 3 in the experimental trials. The same optimization procedure

is repeated for the Y-axis (anterioposterior) and again the segment is

rotated by 1/3 of the angle with the lowest RMS error value. As the

result of rotating each clavicle is calculated separately, not reducing

the optimized rotation by 1/3rd would mean that each segment would

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be rotated to reduce all the error. This would overshoot the necessary

rotation and produce link-segment models that would be visibly

incorrect. These rotations are applied to the clavicle and then the

optimization process is repeated for the upper arms and lower arms in

sequence. Thus the reduction by 1/3rd also ensures a degree of

optimization sharing across segments. Pilot data showed that 6-9

degrees is usually selected for the clavicles about both axes, and 0-2

degrees for the lower arms.

Figure 5 demonstrates this improvement for a trial where the subject

maintained a constant distance of 65cm between the hands. The range

of measurement error is reduced from 17cm to 15cm and the mean

error is reduced from 69 cm to 65 cm, the actual length of the object

used in the optimization trials.

Figure 5 - Comparison of the raw and optimized projected distance

between lower arm endpoints. The actual length of the object is0.65m and represented by the thick black horizontal line

This optimization procedure is performed twice in two conditions:

hands 55 cm and 5 cm apart. The average of the four trials is used as

the optimization value.

As this optimization process, like the spine-sternum optimization

process, reduces RMS error relative to the real hand-to-hand distance,

the process improves accuracy when the predicted values are biased

in one direction, and improves precision when the predicted values

hover around the real value. The remaining noise in the system may

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be due to changes in hand orientation (which was not recorded),

sensor drift, and/or the sensors not moving when the segments were

(as a result of soft-tissue artifact, or the sensors shifting relative to the

clothing underneath).

3.3.4 Determining Loading

The researcher divided every lifting task into distinct phases based on

key loading instances. For hand carries these instances are: bag-

contact, full-bag-support, bag-leaving-hand, and bag-fully-on-ground;

for shoulder carries these instances are: bag-contact, full-bag-support,

hand-to-shoulder-transfer, shoulder-to-hand-transfer, bag-leaving-

hand, and bag-fully-on-ground. These instances define the beginning of

increases and decreases in support of the bag to enable a load timing

vector to be created for each hand and shoulder. These are called load

timing vectors because they change over the course of the trial but are

not vectors in the physical sense as they lack direction. These

instances are determined by visually inspecting every third frame of an

animation of the model in a method similar to watching a video

recording. Once important loading instances were observed the

animation was stopped and other relevant distance, position, or

velocity curves were inspected. Viewing only every third frame was

chosen to speed up the process of load timing determination; however,

when inspecting distances, positions and velocities all frames

surrounding the relevant instance were inspected.

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In all trials the participant (and consequently the animation of the

model representing the participant) initially reaches forward for the

bag. When the researcher determines that the subject has reached

furthest forward the animation is paused and the position curve of the

lower arm endpoint is inspected around this instance. The instance of

maximum reach is determined to be bag-contact. Playback of the

animation is resumed. Then the participant picks up the bag and pulls

it towards themselves. Again the animation is paused, and the

velocities of the relevant hand are inspected. The frame with the

highest peak velocity is chosen as full-bag-support. This decision was

based on the idea that participants would have full control of the bag

when their hand reached its peak velocity after pickup. Additionally,

Eger & Stevenson (2004) have shown that peak vertical hand forces

occurred between 0.07 and 0.18 seconds after the load has been

picked up. Peak vertical hand forces could only occur once the subject

is holding the box in their hands and has a high hand velocity.

The animation is resumed and in the case of shoulder carries, the

animation was again visually inspected up to the point where the hand

came close to the shoulder. At this point the 3D distance between the

lower arm endpoint (hand) and clavicular endpoint (shoulder) was

calculated and the instance of closest proximity was deemed to be the

point of hand-to-shoulder-transfer. This same process was repeated for

shoulder-to-hand-transfer (offloading). After the load was transferred

to the hand the animation was visually inspected for the instance when

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the participant had a substantial downward reach (putting the bag on

the ground). The hand velocities were then inspected for a local

maximum to determine the instance when the bag was starting to be

released, bag-leaving-hand. The height of the hand was then inspected

for the lowest value and this instance was deemed bag-fully-on-

ground.

Separate load timing vectors were then created for the hands and

shoulders from these load timing instances to create smooth load

transitions. From bag-contact to full-bag-support the hand loading

vector was linearly increased from 0 to 1, where at 1 the load had been

fully transferred to the hand. During sagittal plane box lifts, hand force

loading has been shown to increase approximately linearly in instances

where the subject does not push down on the box prior to pick up as

would be the case for lighter loads (Eger & Stevenson, 2004). In the

case of shoulder carries, full-bag-support was maintained in the hand

until the load was transferred to the shoulder. The load in the hand is

decreased linearly while the load on the shoulder increased linearly

during the 3/10ths of a second after the hand-to-shoulder transfer. The

transfer length of 3/10ths of a second was chosen during pilot testing

because the transfer needed to occur in a relatively short period of

time around the hand-to-shoulder transfer instance. Had we elected to

transfer the load over a longer period of time we might have observed

that some of the load was placed in the hand while it was not near the

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shoulder. During the carry phase, the load was either fully in the hand

or on the shoulder.

The process used to determine the loading vector when picking up the

bag was reversed when putting the bag down. For hand carries the

load was maintained until bag-leaving-hand and was then decreased

linearly until bag-fully-on-ground. For shoulder carries, the load was

transferred to the hand at the hand-to-shoulder-transfer and then

decreased until bag-fully-on-ground.

Specific body segment parameters are then added to the model using

subject height, weight, gender, and anthropometric information.

Moment of inertia and centre of mass values are determined based on

Zatsiorskys anthropometrics tables (Zatsiorsky 2002, pp. 304-305,

The Inertial Characteristics of Human Body Segments of 100 Male

Subjects).

The predicted segment endpoint positions were filtered using a

second-order low-pass Butterworth filter with cutoff frequency of 6 Hz

(Dickerson, Hughes & Chaffin, 2008). Segment centre of mass (COM)

positions were determined as a percentage distance between proximal

and distal segment endpoints (Zatsiorsky 2002). Linear COM velocities

and accelerations were calculated from the COM displacements, while

angular velocities and accelerations were calculated by successive

numerical differentiation of the segments angular orientation.

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Using the anatomical model and load timing vectors, a three-

dimensional hands-down, inverse dynamic model calculated the forces

and moments at the L5/S1 joint. These calculations are based on Hof

(1992).

Once the net external moment was computed, a single back extensor

model was used to calculate the force required by the spinal extensors

to balance the external moment. The required force was calculated by

dividing the external moment by a spinal extensor moment arm length

of 6 cm (Norman et al. 1998) and the resultant force was used to

determine the final forces on the L4/5 disc. For a complete breakdown

of the calculations see Appendix A.

The entire process to produce compressive and AP shear force

estimates is presented below.

Figure 6 - Flow chart representing process by which compressiveand AP force estimates are produced. Anthropometric data andoptimization trials are used to produce an optimized participantspecific link segment model. The participant specific LSM is thenused as input to the 3D inverse dynamic model with the data fromeach lifting trial and the generated load timing vectors. Thisproduced compressive and AP shear force estimates for each trial.

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3.4 Methods

A comparison was required against a validated model to ensure that

our model could accurately determine the joint loading during the

lifting tasks. For this comparison we used the 3-Dimensional Static

Strength Prediction Program (3DSSPP) developed by the University of

Michigan. 3DSSPP is a software program that uses postural

information, relevant anthropometric variables and force input to

determine joint forces during a task. The 3DSSPPs output includes:

compressive forces and moments at the L5/S1 joint, shoulders, elbows,

and hands, as well as reports predicting the percentage of the

population that are able to safely complete the task. 3DSSPP is unable

to incorporate dynamic components of lifting and is accurate when the

rate of lifting is below 3 Hz. 3DSPP has been validated as a strength

prediction assessment tool with a correlation greater than 0.85 with

actual strength data; however, it is sensitive to postural input errors

(Chaffin & Erig 1991; Chaffin 1992).

A series of lifting trials was simultaneously recorded using 3DSSPP and

our model. These trials consisted of a simple box lift requiring the

subject to pick a 10kg box off of a table, touching it to the ground and

returning the box to its original position on the table. Two versions of

our model are presented, a fully dynamic model that we used for

paramedic testing and a 3DSSPP-matched static model. This was

necessary since 3DSSPP is a static model and the segment lengths it

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uses are based on the entered subject height and cannot be

manipulated individually.

3.5 Results

Figure 7 - Presentation of the compressive forces on the L5/S1 jointas a result of a stoop lift as calculated by 3DSSPP, a static-linkedsegment model designed to match 3DSSPP and a fully dynamic linksegment model.

As can be seen in Figure 7 through 10 the 3DSSPP matched link

segment model follows the 3DSSPP curve for compressive and

anterior-posterior (AP) shear forces. This is particularly critical as these

are two of the main outcome measures of this study. One area of

obvious disagreement between 3DSSPP and our LSM is in the middle of

the trial, when the participant is placing the box on the ground. This is

only the case for compressive force estimates as AP shear forces

estimates seem to be in close agreement throughout the entire trial.

During the middle of the trial the subject is squatting down and

touching the box to the ground. At this instance we should see slightly

lower compressive force estimates than at pickup as the weight is held

closer to the body and the participant is no longer reaching forward.

The difficulty in matching the mannequin in 3DSSPP to the observed

posture could account for some of this error.

Figure 8 - Presentation of the compressive forces on the L5/S1 jointas a result of a squat lift as calculated by 3DSSPP, a static-linked

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segment model designed to match 3DSSPP and a fully dynamic linksegment model.

Figure 9 - Anterior-posterior force on the L5/S1 joint as a result of astoop lift; calculated from 3DSSPP and two link segment models.Positive values represent a tendency to translate anteriorly.

Figure 10 Anterior-posterior force on the L5/S1 joint as a result of asquat lift; calculated from 3DSPP and two link segment models.Positive values represent a tendency to translate anteriorly.

3.6 Discussion

We are pleased with the similarity between the compressive and AP

shear force curves from 3DSSPP, the 3DSSPP-matched static link

segment model and the dynamic link segment model. One obvious

disagreement between 3DSSP and our models occurs near the middle

of all trials; this is the instance when the box is closest to the ground.

We hypothesize that the difference in this phase of the lift is due to

how each model represents the back. 3DSSP has a single segment

representing the entire back while our model uses two segments;

3DSSPP therefore does not consider thoracic flexion and as such some

information may be lost.

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In the case of the stoop lift, the maximum forward flexion measured in

our model was approximately 100 of flexion for the lumbar segment

and 140 for the thoracic segment, while 3DSSPP measured 100 of

flexion for the single trunk segment. For our model, the additional 40

of forward flexion in the upper trunk causes a shorter moment arm

thereby reducing the external moment. Therefore, the reaction

moment that is required to balance the system is lower and leads to

lower force estimates. The differences can be seen in Figure 11, as the

participant rounds out their shoulders when touching the box to the

ground.

Figure 11 - Three representations of the same position of a trial. Onthe left the participant touching a 10kg box to the ground as part ofa stoop lift. In the centre the same image overlaid with a mannequinfrom 3DSSPP. On the right the Link Segment Model.

In Figure 11 it is apparent how the differences between the flexion

angles come about. The upper back sensor on the participant is tipped

very far forward and may be in excess of the actual flexion of the

thoracic segment. The 3DSSPP model has no forward flexion at this

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point and, as a result, the mannequin is closer to 100 of flexion

requiring a greater muscular effort to balance the system than the

140 seen in the link-segment model. It would seem that the flexion of

the thoracic segment that 3DSSPP lacks may be driving some of the

differences between the models.

In order to understand how differences in back representations would

influence paramedic research we calculated the average flexion angle

at the peak loading instance for all paramedic trials. At peak loading no

participant was flexed more than 80 so we compared 3DSSP and our

LSM during a stoop and squat lift when both models were at 80. Most

participants average peak flexion was around 60 so we compared our

model against 3DSSPP at this angle as well.

Figure 12 - Compressive and AP shear force LSM estimates of astoop and squat lift at 60, 80, and 100 degrees of upper back flexion

represented as a percentage of those values found using 3DSSPP.

As trunk flexion increases past 60 degrees our dynamic LSM force

estimates and 3DSSPP force estimates begin to diverge. As flexion

reaches 100 degrees this difference reaches almost a 1000N

discrepancy. However, we believe that the true compressive force

value lies somewhere between our value and 3DSSPPs at this angle as

trunk flexion, especially thoracic flexion, appears decreased in the

3DSSPP model but increased in ours as a result of the shoulders

rounding out. Given that most paramedics were not required to go

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into high degrees of trunk flexion this is less of an issue for our

research than the stoop and squat lift comparisons present.

Additionally, the high degree of agreement (greater than 80% for

much of the trial) between 3DSSPP and our model shows that we can

as validly represent human motion and calculate the resultant forces

within the range of 0-60 of forward flexion.

It appears that sensor placement can be an issue when it comes

estimating low back loading especially in the case of large thoracic

flexion such as when touching the ground. If the sensor is placed too

low on the thoracic spine then thoracic flexion is underestimated, but if

the sensor is placed too high then flexion is overestimated. This may

provide a direction for future research to determine the best sensor

location for the thoracic segment to achieve best estimates of thoracic

flexion.

We have shown that our link-segment model is a valid tool to represent

human motion. We also believe that our novel shoulder model is more

appropriate than static shoulder models in reflecting shoulder joint

translation. If we assume that our shoulder model more truly

represents what actually happens, and this is then used as input for a

dynamic inverse model, then we should expect that more valid loading

estimates are the result.

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Chapter 4: Paramedic Lifting

4.1 Introduction

The 72 certified land ambulance services in Ontario respond to an

estimated 1.5 million calls annually. In responding to these calls

paramedics carry their equipment bags over long distances, up and

down stairs, and through confined spaces. Given that paramedics can

respond to a number of calls per shift it is not surprising that the

various types, size, and weights of these bags are a concern. Despite

the fact that common equipment is carried in the bags, there are no

standards governing the size and number of bags or the weight carried

in any particular bag. The Association of Municipal Emergency Medical

Services of Ontario (AMEMSO) as part of its responsibilities to its

members, wanted to investigate bag lifting as a possible cause for

concern regarding low back injury rates.

In addition to developing contacts with regional paramedic services,

initial meetings were scheduled with AMEMSO President Paul

Charbonneau to increase our understanding of the paramedic

population based on his opinion of: number of hours worked per week,

number of calls per shift, shift timing, general fitness of paramedics,

general attitude of paramedics, speed of work, and general complaints

made by paramedics regarding their working conditions. These

meetings lead to visits to paramedic services in Kingston, Waterloo,

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Sudbury-Manitoulin and Greater Sudbury during the month of March to

record the range of bag weights, designs and lifting styles.

Prior to collecting information, letters of understanding and permission

were signed by a representative from each participating service as well

as all participating paramedics, as approved by the Queens General

Research Ethics Board. Paramedics were asked to fill out a

questionnaire as well as answer some questions during an informal

interview. During these interviews paramedics were asked which

aspects of bag lifting they felt were most demanding, as well as how

they felt bag lifting ranked in relation to other demanding tasks

associated with the job. Furthermore, some services participated in

pilot lifting trials on-site similar to those performed later in the lab.

These pilot testing sessions were used to get a sense of the types of

motions performed by paramedics as well as learn if the paramedics

felt comfortable performing the testing protocol. In total eight subjects

participated in pilot testing and 16 participated in interviews or filled

out questionnaires.

These meetings and pilot testing sessions, along with information

gained from a questionnaire sent out to paramedics by Dr. Renee

McPhee (personel communication), led to an understanding of the

general working demands of paramedics across the province.

It was found that:

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Paramedics frequently carry bags ranging from 1-20kg, with most

bags in the range of 5-12kg.

When responding to calls usually more than one bag is carried.

When loading out, bags are often picked up off the stretcher at

about waist height, carried to the person in need and then placed

on the ground.

When loading in, bags are picked up off the ground then placed on

the stretcher or carried by the paramedic.

Heavy bags and bags requiring awkward postures were the biggest

complaint, partially due to uneven loading in the bags or carrying

too much equipment, some of which is rarely used.

Two common suggestions were received: reducing the amount of

weight in the bags and move towards backpack style bags when

possible. One participant suggested the use of wheelie bags to

reduce the demand on paramedics.

These insights led to the development of an in-lab testing protocol that

was designed to reflect the bag lifting demand experienced by

paramedics.

4.2 Review

Lifting has been identified as a hazard that can lead to low back pain

(Marras et al. 1995; Chaffin & Park 1973). Paramedics share similar

lifting task demands with a variety of professions such as nurses,

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nursing aides, and fire-fighters who are required to support a patient

and administer some level of care while simultaneously transporting

the tools necessary to provide that care. Studies of the nursing

population have shown high levels of low-back pain much of which is

related to patient handling, (Jensen 1987, Videman 1984) a task that

paramedics are frequently required to perform with, potentially,

greater strain due to awkward postures and lower lift origins.

Back injuries are very common in the paramedic population accounting

for 36% of total injuries; of those more than half were caused by lifting

activities (Hogya & Ellis 1990). Crill & Hostler (2005) surveyed EMS

providers and found that almost 20% had reported a back injury while

performing EMS work in the previous six months. It is important to

understand which aspects of paramedic work lead to low back pain.

Measures that have been correlated with increased low back pain

include: peak compressive force, and peak shear force experienced by

the L4/L5 or L5/S1 joint, (Norman et al. 1998; van Dien & Toussaint

1997). The National Institute for Occupational Safety and Health has

identified two limits for compressive force on the lower back based on

biomechanical, psychosocial, and physiological research. 3400N has

been put forward as an acceptable limit below which most workers

should be able to work safely for long periods of time without injury

(NIOSH 1981). A maximum permissible limit of 6400N was established

beyond which individuals cannot work without injury. Waters et al

(1993) argue that if the data were normally distributed 21%-30% of

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lumbar segments would fail when loaded with a force of 3400N given

the ultimate compressive force values put forward by Brinckmann et al

(1988) and Jger & Luttman (1989). The limit of 3400N is a

conservative estimate for a healthy working population since cadaver

lumbar segments may have lower tolerance limits (Hansson & Roos

1981); however, when ensuring workplace safety conservative limits

should be used.

Shear forces act in the medial-lateral and anterioposterior directions

along the spine and are a risk factor for low back pain (Kerr et al.

2001). Krypton et al. (1995) found shear force tolerance limits in

cadavers of between 1700N and 2900N. While less work has been

done in this area, some guidelines are based on thinking similar to that

of the NIOSH Equation. McGill et al. (1998) put forward an action limit

of 500N and a maximum permissible limit of 1000N. A shear force limit

of 500N has been used with reasonably accuracy to predict which

workers reported low back pain (Daynard et al. 2001). Motions that

keep compressive forces below 3400N and shear forces below 500N

are unlikely to increase the risk of injuries.

Clearly, it is important to understand the loads generated by

paramedics across a variety of lifting conditions. An understanding of

the magnitude of these loads while performing paramedic work is

essential to determine if the weights and/or lifting techniques are safe.

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4.3 Methods

4.3.1 Participants

Twenty-five participants were recruited from the Kingston paramedic

community. All volunteers reported no low back, shoulder, arm, hand

and wrist pain in the last year. Informed consent, approved by the

Queens University General Research Ethics Board, (Appendix B) was

given before testing. Participants were provided a $50 honorarium to

compensate for travel and parking.

4.3.2Instrumentation

Prior to testing, subjects were outfitted with the Xsens Motion Tracking

System, a wireless motion tracking system with sensors that combine

accelerometers, gyroscopes and magnetometers to determine a

sensors orientation. Due to hardware difficulties that caused two

sensors to be unusable and that no additional sensors were available,

the instrumentation setup was altered from that used in model

development. It was decided that the scapular sensors were the least

important since their range of motion was the smallest. As a result only

6 sensors were used for paramedic testing.

Data obtained from pilot testing was reevaluated by reducing the

number of sensors used in creating the link-segment model, to test the

influence of the novel link-segment model on force outputs during

lifting and carrying tasks. Visual inspection showed of force curves

showed changes of less than 10% when removing the scapular

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sensors. As a result we could assume that our model was still valid

when using only 6 sensors.

Subjects were instrumented with sensors placed on the lower back,

upper back, upper arms, and lower arms. When attached to the

aforementioned limbs the motion of the participant can be tracked.

Instead of determining clavicular orientation using Xsens sensors, the

clavicle segment was projected straight out from the upper back

segment based on the direction of the medial-lateral axis of the upper

back segment. The clavicle, upper arm, and lower arm segments were

all optimized in the same way as explained in Section 3.3.3.

4.3.3Bags

Four EMS bags (provided by the Frontenac Paramedic Service) were

acquired and loaded with weights of 5kg, 10kg, 15kg and 20kg. The

5kg and 10kg bags were carried as handbags, the 15kg bag was

carried as a shoulder bag, and a backpack was loaded with 20kg.

These bags are shown in Figure 13.

Figure 13 - EMS bags used during in-lab testing

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4.3.4 Lifting Tasks

Two conditions were developed for the 5kg and 10kg bags, the first

condition had the bag placed on the edge of a table as close to the

participant as possible while in the second condition the bag was

placed approximately 50cm away from the edge of a 78 cm high table,

requiring paramedics to reach forward. A height of 78 cm was used as

it was between the height of the stretcher and back of a paramedic

SUV from which paramedics lift their bags. Two conditions were also

developed for the 20kg backpack; the first required the participant to

pick up the bag and sling it over one shoulder; the second required the

participant to carry the bags like a normal backpack (i.e. using both

shoulder straps). In total there were seven lifting conditions (two each

for 5kg, 10kg and 20kg bags, one for 15kg bag). For most lifting

conditions the participant picked the bag up off of a 78cm high desk,

carried it 4m and placed it on the ground. This mimics arriving at the

scene, removing the bag from the back of the vehicle or off of a

stretcher and carrying it to the site. The exception to this was the

double strap shoulder backpack carry where the experimenter helped

the subject load the backpack onto their shoulders, which avoided

disrupting the sensors placed on the subjects back.

Simulating carrying the bags back to the ambulance was not done. A

list of these conditions is presented in Table 1.

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Table 1 - Conditions presented to paramedics during in-lab testing.All conditions included a 4m carry.

2

Bag Bag Size

HxWxD

(cm)

Weight

(kg)

Lift Type LiftOriginHeight

ForwardReach

Distance

LiftDestinat

n

YellowALS Bag 23 x 32 x 12 5 Hand

78cm 0cm Floor

78cm 50cm Floor

BlueOXYGEN

Bag33 x 58 x 24 10 Hand

78cm 0cm Floor

78cm 50cm Floor

RedDuffelBag

28 x 52 x 34 15Single Strap

Shoulder78cm 0 cm Floor

OrangeBackpac

k55 x35 x20 20

Single StrapShoulder

78cm 0cm Floor

DoubleStrap

N/A N/A N/A


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4.3.5 Data Collection

Each subject was asked to perform each condition four times,

producing 28 trials per subject. Due to a combination of hardware and

software problems four subjects performed fewer than 28 trials. In total

21 subjects completed all 28 trials, while all 25 subjects completed a

minimum of 22 trials. The participants height, weight, gender and

relevant anthropometric measurements were recorded after testing.

Participant anthropometrics are listed in Appendix C. Sensor

orientation was recorded using MT Manager software (Xsens

Technologies, Netherlands). Each trial was recorded and exported

individually before analysis.

Figure 14 - Representation of trial setup. Participants were requiredto lift a bag from a height of 78 cm, at a reach distance of 0 cm or50cm, and then carry it 4m before placing it on the ground.

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After testing, participants were asked to comment on how closely the

lifting protocol reflected their day-to-day lifting tasks. Most participants

said that the general lifting requirements were accurately represented;

however, they felt that the wide variety of lifting demands (i.e. stairs,

small doorways, multiple bags) were not present in testing (for all

responses see Appendix D).

4.3.6 Data Processing & Statistical Analysis

All trials for all subjects were visually inspected to determine load

timing phases and create loading vectors as explained in Chapter

3.3.4. All data processing was performed in Matlab (R2009a, The

MathWorks). The output from each trial was time-normalized to

create load timing phase consistency across trials and subjects through

piecewise normalization. In this process each phase of the trial is

allotted a specific percentage of the trial and is linearly interpolated

between relevant load timing instances. This process allows

subsequent ensemble averaging the phases without any phase shifts.

For statistical analysis, relevant data were extracted from curve

profiles for each trial. These were: peak compressive force at bag

pickup, trunk lean at bag pickup, forward reach at bag pickup, shoulde

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