Ambient Activity Monitoring System

Ambient Activity Monitoring System using a Tri-axial

Accelerometer

by

Keith Connaughton

This Report is submitted in partial fulfilment of the requirements of the

Honours Degree in Electrical and Electronic Engineering (DT021) of the

Dublin Institute of Technology

May 31st, 2013

Supervisors: Dr. Ted Burke and Dr. Damon Berry

Ambient Activity Monitoring System

i

Abstract

This project presents a prototype of a wireless activity monitoring system for human

activity classification. The system consists of two main parts; the first is the wireless

activity monitoring sensor (WAMS), which consists of a tri-axial accelerometer, a

microcontroller (dspic30f4011) and wireless XBee communications module. The

second is the activity classification program (MATLAB), which is used to identify

human activity using the accelerometer data from the sensor. The system has

wireless communication capabilities and the network topology includes a base

station connected to a personal computer.

The prototype sensor is adaptable to household furnishings to monitor human

activity in a nonintrusive way. For this project, the wireless sensor is attached to an

office chair to measure human activity. The system uses a classification algorithm

that employs a neural network model for recognising a set of human activities on an

office chair. This algorithm is capable of distinguishing three human activities on an

office chair (sitting, getting up and unoccupied).


ii

Declaration

I hereby declare that this thesis is my own work and effort and that it has not been

submitted anywhere for any award. Where other sources of information have been

used, they have been acknowledged.

Signature: ……………………………………………………

Date: ……………………………………………………..


iii

Acknowledgements

I would like to thank my supervisors, Dr. Ted Burke and Dr. Damon Berry for their

guidance and support during my project and study at Dublin Institute of Technology.

Ted and Damon were always accessible and willing to help with my research,

making the project experience a smooth and a rewarding one. I want to sincerely

thank Dr. Ted Burke for his blog on dspic microcontrollers (Batchloaf WordPress)

which was an invaluable resource for programming microcontrollers (dsp30f4011).

Ambient Activity Monitoring System Table of Contents

iv

Table of Contents

1.1 Introduction .................................................................................................... 1

1.2 Project Concept and Objectives ..................................................................... 2

1.3 Thesis Structure ............................................................................................. 4

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

3.1 Hardware Design ........................................................................................... 6

3.1.1 Three-axis Accelerometer (MMA736L) ................................................. 7

3.1.2 Microcontroller dsPIC30F4011 ............................................................. 7

3.1.3 Wireless Communication ...................................................................... 9

4.0 Software Design and Algorithms .................................................................. 10

4.1 Microcontroller (dsPIC30F4011) Program ................................................... 10

4.2 Classification Algorithm ................................................................................ 12

4.2.1 FIR Low-pass Filter............................................................................. 15

4.2.2 Feature Extraction Techniques ........................................................... 16

4.3 MATLAB Program ........................................................................................ 18

5.0 Testing and Validation ................................................................................. 20

5.1 Data Description .......................................................................................... 20

5.2 Feature Extraction ........................................................................................ 22

5.3 Human Activity Classification Model for Office Chair ................................... 24

Chapter 1 1

Chapter 2 5

Chapter 3 6

Chapter 4 10

Chapter 5 20

Ambient Activity Monitoring System Table of Contents

v

6.0 Discussion.................................................................................................... 25

6.1 Conclusion ................................................................................................... 26

Appendix A – Activity Monitoring Sensor Schematic.......................................... 28

Appendix B – Microcontroller Program (dsPIC30F4011) ................................... 29

Appendix C – MATLAB Program ....................................................................... 32

Appendix D – Zero-crossing() MATLAB Function .............................................. 35

Appendix E - Training Data Results ................................................................... 36

Chapter 6 25

Bibliography 27

Appendices 28

Ambient Activity Monitoring System Table of Figures

vi

Table of Figures

Figure 1.1: System Block Diagram ............................................................................. 3

Figure 3.1: Sensor Prototype and System Overview .................................................. 6

Figure 3.1.3: XBee Series 2 Schematic ...................................................................... 9

Figure 4.1: Control Flow Diagram for the Microcontroller Program .......................... 12

Figure 4.2: Block Diagram for Classification Algorithm ............................................. 14

Figure 4.2.1: Filtered Accelerometer Data ................................................................ 15

Figure 4.3: Accelerometer Data ............................................................................... 19

Figure 5.1.1: Accelerometer Data for Unoccupied Office Chair ................................ 21

Figure 5.1.2: Accelerometer Data for Sitting Activity on Office Chair ....................... 21

Figure 5.1.3: Accelerometer Data for Getting-up Activity on Office Chair................. 22

Figure 5.2: Feature Extraction Data for Human Activity on an Office Chair .............. 23

Figure 5.3: Feature Extraction Data with Linear Neural Network Model ................... 24

Figure E.1: Accelerometer Data for Unoccupied Office Chair .................................. 36






Figure E.7: Accelerometer Data for Sitting Activity on Office Chair .......................... 39



Figure E.10: Accelerometer Data for Sitting Activity on Office Chair ........................ 40



Figure E.13: Accelerometer Data for Getting-up Activity on Office Chair ................. 42






Ambient Activity Monitoring System Chapter 1

1

Chapter 1

1.1 Introduction

Human activity monitoring has become a huge area of research in recent years. It is

a prominent and difficult field that can support many novel applications. Typical

applications are smart homes, home healthcare, elderly fall detection systems and

office ergonomics. Activity monitoring is a research area that involves technologies

such as machine learning and human computer interaction.

The objective of an activity monitoring system is to identify the activities of its user in

an none intrusive way. For example, an activity monitoring system in a home

environment can monitor a user’s activities to remind them to perform forgotten

activities or actions such as taking medicine or encourage them to act more safely

(1). Activity recognition systems in hospital environments could be utilized to remind

doctors or nurses to perform certain tests before operating (2).

The ability to recognize different activities seems to be easy and natural for people

but it involves the difficult task of sensing, learning and interfacing (2). Moreover,

identifying different human activities is a difficult challenge for automated activity

monitoring machines and systems and it will require the system to sense the ambient

environment changes, learn from experiences and to classify the activity correctly.

Therefore, the aim of the ambient activity monitoring system is to enable machines to

have similar capabilities as people for identifying human activities (2).


2

1.2 Project Concept and Objectives

The objective of this project is to design an ambient recognition system that can

automatically recognize human activities using a 3-axis accelerometer. The

proposed system is designed to be none intrusive to the user and it should allow the

sensor to be attachable to household furniture, doors and medical cabinets for

human activity monitoring.

The system consists of two main parts; the first is the wireless activity monitoring

sensor (WAMS), which consists of a three-axis accelerometer, a microcontroller

(dspic30f4011) and wireless XBee communication module. The second is the activity

monitoring algorithm (MATLAB) which is used to monitor and train the sensor.

The system is implemented for multi-user activity monitoring. Multi-user activity

monitoring means training the system based on observed data to classify different

human activities (e.g. someone opening a door or someone sitting on an office

chair), more than one user can train the system for multi-user activity monitoring.

Initially, the system is trained by the user to identify different human activities but

once the algorithm has been trained, it uses the raw data from the sensor to classify

the action or activity.

Figure 1.1 shows a block diagram of how the data is processed in the system. A

laptop is used for data acquisition and analysis. An office chair is equipped with the

activity monitoring sensor. Raw accelerometer data is sent from the sensor to the

laptop wirelessly using XBee modules. The laptop will be equipped with a XBee USB

dongle in order to create the wireless communication link between the sensor and

the MATLAB application. Once the system has been trained to detect three classes

of human activity (sitting down, unoccupied and getting up) on the office chair, the

MATLAB program will receive the raw accelerometer data from the sensor and it will

provide the user with the activity classification.


3

0 5 10 15-0.2

0

0.2

Time (sec)

X Accelerometer Data

0 5 10 15-0.5

0

0.5

Time (sec)

Y Accelerometer Data

0 5 10 15-1

0

1

Time (sec)

Z Accelerometer Data

Figure 1.1: System Block Diagram


4

1.3 Thesis Structure

The subsequent chapters of this thesis are organized as follows:

• The literature review in Chapter 2 discusses different ambient and wearable

activity monitoring systems and it describes the systems used to classify

human activity.

• Chapter 3 describes the hardware architecture of the ambient activity

monitoring system and its various components.

• Chapter 4 gives an in-depth description of the software implementation, the

software algorithms, the digital signal processing and the activity monitoring

classifications (Neural Network).

• Chapter 5 explores the testing of the system for an office chair, explaining the

strategies used and the results obtained.

• Chapter 6 finishes with a discussion and a conclusion followed by a list of

references and a complete set of appendices. The code written for the

microcontroller and the MATLAB program are in the appendices of the report

and they are referred to throughout this thesis.


5

Chapter 2

2.0 Literature Review

Human activity monitoring can be categorized into ambient monitoring systems and

wearable monitoring systems (3). Since these techniques require actual human

observations or video cameras or measuring devices worn by the user, these

techniques can be grouped in a direct way to record human activity.

The activity monitoring system described by (4) is made up of three cameras. They

operate either as a set of three static cameras or as a set of one fixed camera and

an active binocular vision system. The human activity is monitored by extracting

several features that are useful for the activity classification. The system has a

database, which allows the type of activity to be identified.

The activity monitoring system described by (5) provides a way of monitoring and

identifying elderly people’s activities in the home environment. The system consists

of sensors attached to household furnishings and video cameras to record the

different activities. This system combines the data provided by the video cameras

with the data provided by the sensors to recognise the daily activities of the elderly

people.

The environmental sensor based system described by (6) is achieved by distributing

a number of ambient sensors, especially binary on-off state sensors, throughout the

subject’s living environment. The data gathered by these environmental sensors can

be used to adapt to the user’s environment. The sensors passively monitor the

occupants every day, therefore require no action on the part of the user to operate.

The ambient sensors transmit data wirelessly to a local PC where the data is

processed and the activity is logged to a database.


6

Chapter 3

3.1 Hardware Design

The Wireless Activity Monitoring Sensor (WAMS) provides ambient activity

monitoring using a 3-axial accelerometer. The main reason why an accelerometer

was chosen compared to other sensors (Passive Infra Red, etc) was its versatile

capabilities. An accelerometer can measure both dynamic and static acceleration (tilt

angle) making it adaptable to many activity monitoring applications. The prototype

sensor is housed in a plastic enclosure and it has four main components. They are a

3-axis accelerometer (MMA7361L), a microcontroller (dspic30f4011 Microchip), a

XBee (Series 2) wireless communication module and a AA battery pack. The activity

monitoring sensor has a supply voltage of 3 volts and it draws 100mA when

transmitting data continuously. A laptop is equipped with a XBee USB dongle to

receive data from the sensor. Figure 2.1 shows an overview of the system

components.

Figure 3.1: Sensor Prototype and System Overview

Microcontroller XBee Module

3 V Supply Accelerometer

Wireless Communication Network

XBee USB Dongle


7

3.1.1 Three-axis Accelerometer (MMA736L)

The MMA736L is a small, thin, low power, three-axis analog accelerometer with

signal conditioned voltage outputs, all on a single monolithic integrated circuit. This

device is set to measure acceleration at +/- 1.5g with a sensitivity of 800 mV/g. The

MMA736L accelerometer uses a single structure for sensing the X, Y and Z axes. It

can measure the static acceleration of gravity in a tilt-sensing application as well as

dynamic acceleration resulting from motion, shock or vibration.

The output signals X, Y and Z are analog voltages that are proportional to

acceleration. The MMA736L accelerometer has an operating voltage between 2.7-

3.4 volts.

3.1.2 Microcontroller dsPIC30F4011

The dsPIC30F4011 microcontroller is an inexpensive single-chip computer that has

I/O circuitry and peripherals built-in, allowing it to interface with real-world devices.

The microcontroller contains a CPU, RAM, ROM, discrete I/O, serial and parallel

ports, timer interrupts, analog-to-digitals and digital-to-analog converters. The

dsPIC30F4011 was chosen for this project for the following reasons:

• Supply voltage range (2.5-5.5V)

• 9 analog input channels (10-bit)

• 2 UART channels

• Multiple digital I/O (inputs and outputs) pins

• Cheap and reliable

• C programmable


8

The clock speed for the microcontroller is set to 20 MIPS (Million Instructions Per

Second). An external 10 MHz crystal oscillator was used to provide a stable CPU

clock frequency. This allows the A/D channels to be sampled at precise time

intervals using a timer interrupt. A serial communication link was established

between the microcontroller and the XBee module using UART (Universal

Asynchronous Reciever Transmitter).

UART is a piece of hardware that translates data between devices. The UART

converts bytes of data to bits and it transmits the data. At the destination, a second

UART converts the bits into bytes. For this project, the baud rate is set to 57600 bits

per second.

MMA736L, a three axis accelerometer provides analog voltages on three axes X, Y

and Z, which are proportional to acceleration. As the accelerometer generates

analog voltages, it is interfaced to the analog pins of the dsPIC30F4011. The X, Y

and Z pins of the accelerometer are connected to the AN0, AN1 and AN2 of the

microcontroller. The microcontroller samples the analog voltages on the three A/D

channels and converts the analog signal to a 10-bit digital number. This data is then

transmitted serially to the XBee module.

The A/D converter on the microcontroller has a unique feature of operating in sleep

mode by using the conversion clock from the A/D internal oscillator. This is a useful

feature for power saving when the sensor is not active. This would allow the overall

power consumption of the sensor to be reduced.


9

3.1.3 Wireless Communication

Various protocols for wireless communications are available to transfer data from

microcontrollers such as Bluetooth, ZigBee and MiWi. For this project, a ZigBee

protocol is used because it adds additional routing and networking functionality.

XBee (Series 2) is a wireless transceiver that has the ZigBee protocol embedded in

its firmware. Interfacing with the XBee requires the use of the UART protocol. Bytes

of data will be sent out from the microcontroller to the remote XBee transceiver The

XBee transceiver will send the data to its corresponding XBee transceiver (USB

XBee dongle) at the Laptop. XBee was chosen because it has low power

consumption (1mW) and it has a range of 50 metres. It operates at a voltage range

of 2.9 to 3.3 V DC. The transceivers are configured as a point-to-point wireless

topology but it can be configured as point-to-mulipoint topology when more sensors

are integrated into the system. The software used for programming XBee modules is

X-CTU. This software allows the user to configure the XBee modules. Both XBee

modules have a baud rate of 57600 bits/sec. Figure 2.1.3 shows the schematic

diagram for the XBee module.

Figure 3.1.3: XBee Series 2 Schematic


10

Chapter 4

4.0 Software Design and Algorithms

The full commented MATLAB and dsPIC30F4011 code can be found in Appendix B

and C respectively. The code for the dsPIC microcontroller includes functions to

configure 3 AD channels, UART and a timer interrupt. The MATLAB code includes

serial port configuration, digital signal processing of accelerometer data and the

activity monitoring algorithm.

4.1 Microcontroller (dsPIC30F4011) Program

The microcontroller is programmed in C. The two main peripherals used are the ADC

and UART. These peripherals along with the algorithm for sending the accelerometer

data to the laptop will be discussed.

There are two main functions that were created for the microcontroller program:

configure_pins and read_analog_channel. The main function enters an infinite while

loop after executing the configure_pins function. The configure_pins function sets the

initial values of the I/O ports of the microcontroller and it configures the UART

settings, baud rate (57600 bits/sec), the analog inputs and the timer interrupt service

routine.

The three analog channels connected to the accelerometer are converted into a

digital number. The analog voltage is read through the read_analog_channel

function. The channel is defined in the function variable and is sampled, converted

and returned as a 10-bit integer value. Therefore, the AD range is 0 to 1024 on a 3v

scale which results in 2.9 mV resolution.


11

Branching off the infinite while loop, there is a timer Interrupt Service Routine (ISR)

that samples the three AD channels and transmits the data to the XBee module

using UART. To ensure the AD channels are sampled at regular time intervals, the

timer interrupt service routine is invoked every 5ms (200Hz). Figure 4.1 shows the

control flow diagram for the microcontroller program.


12

Figure 4.1: Control Flow Diagram for the Microcontroller Program

4.2 Classification Algorithm

A classifier is an algorithm that analyses an observation (e.g. set of measurements

from the accelerometer) and assigns the observation (accelerometer data from

human activity) to one of a number of classes. For instance, the classifier for this

project is human activity monitoring on an office chair and the classes relating to the

classifier are sitting, getting up and unoccupied.

Feature extraction is a term used in signal processing and it refers to a process of

obtaining different signal attributes such as mean, standard deviation, peak to peak

amplitude, zero crossing, frequency content, maximum Root Mean Square (RMS)

amplitude and maximum peak amplitude.


13

Feature extraction techniques obtain specific information and are used to build

classification models (Neural Network). These techniques can be used to

discriminate different human activities from the accelerometer data. Two techniques

were chosen to identify human activity. They are RMS amplitude and zero crossing

using the tri-axial accelerometer data.

For this project, accelerometer data is used to recognize human activity types. The

classification algorithm was developed to identify human activity on an office chair

with a tri-axial accelerometer. Training was done on a small set of data collected

from observational testing of the accelerometer sensor. The classification algorithm

was trained to distinguish between three activity classes thought to be common to

office chair occupancy: sitting, getting up and unoccupied chair.

Figure 4.2 shows the prototype system for human activity classification. First the

accelerometer measures movement on the office chair. Next, the accelerometer’s

signals are filtered in MATLAB without losing relevant information. Two features are

extracted from the filtered data using digital signal processing techniques (zero

crossing and RMS peak amplitude).

A neural network model was created in MATLAB with the accelerometer training

data. The neural network has a two input/output model. The two features (zero

crossing and peak RMS) are passed to the neural network model and it determines

the class (e.g. unoccupied chair) based on the accelerometer training data. Once the

class has been identified, the neural network outputs two binary numbers that

represents the identified class.


14

0 5 10 15-0.2

0

0.2

Time (sec)


0 5 10 15-0.5

0

0.5

Time (sec)


0 5 10 15-1

0

1

Time (sec)


Figure 4.2: Block Diagram for Classification Algorithm


15

4.2.1 FIR Low-pass Filter

Given a finite duration of nonzero input values, a FIR filter will always have a finite

duration of nonzero output values, and that is how FIR (Finite Impulse Response)

filters got their name. The FIR non-recursive difference equation is shown below.

[ ]M1

K 10

y[n] = b x n-K where M is finiteK =

∑

The signals (X, Y and Z) coming from the accelerometer contains noise interference.

If the signal features are to be extracted from the accelerometer data, the signals

needs to be filtered first or else the data used will be erroneous. The FIR filter allows

low frequency signals pass through the filter and it blocks high frequency signals.

Figure 4.2.1 shows the raw accelerometer signal compared to the filtered

accelerometer signal. Clearly, the high frequency noise has been eliminated leaving

a smooth accelerometer signal.

FIR Low-pass Filter Design Specifications:

• Pass Band Frequency: 35 Hz

• Stop Band frequency: 45 Hz

• Sampling Frequency 200 Hz (200 samples/second)

Figure 4.2.1: Filtered Accelerometer Data

0 5 10 15-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

Time (sec)

X-axis Accelerometer Data

Am

plit

ud

e (G

s)

0 5 10 15-0.1

-0.05

0

0.05

0.1

Time (sec)

Filtered X-axis Accelerometer Data

Am

plit

ud

e (G

s)


16

4.2.2 Feature Extraction Techniques

Zero crossing and RMS (Root Mean Square) are used to extract signal information

from raw accelerometer data. In addition, these metrics are used as preprocessing

steps to select key signal features for the neural network model. These techniques

are used to measure human activity levels on an office chair.

The root mean square (RMS) of a signal Xn represents a sequence of n discrete

values is obtained using the following equation:

2 2 2

1 2 + + = n

RMS

x x xX

n

For further processing, the acceleration vector of the three axis are combined to give

the RMS acceleration for the office chair. The RMS equation for the three axes is

shown below where XAcc, YAcc and ZAcc are the corresponding office chair

acceleration vectors.

2 2 2RMS = + Y + ZAcc Acc AccX

Zero-crossing is the points where the signal passes through a specific value

corresponding to half of the signal range. The reference value can be either the

mean value of the accelerometer (gravitational offset) or the extracted mean value (0

reference). The extracted mean value is used to give a reference of zero. The

number of times the signal crosses the reference value is the number of zero-

crossings. A MATLAB function (zero_crossings) was created to count the number of

zero-crossing in the accelerometer data.

In order to calculate the acceleration of the individual axes, the 10 bit digital number

(A/D microcontroller) is converted into an analog voltage in the MATLAB program.

These analog voltages are scaled to engineering units (g-force acceleration) using

the accelerometer sensitivity value and reference voltage.


17

10BV

BV

BV

B B B

2

(X * 3)X = (3 volt supply and 2 = 1024)

1024

(Y * 3)Y =

1024

(Z * 3)Z =

1024

X ,Y and Z are the binary values read through the serial port on the laptop

mAcceleration Calculations ( )sec

Accelero

VAcc

VAcc

VAcc

meter sensitivity = 0.8 V/g

voltage_ref = 1.5 volts

(X - voltage_ref)X =

Accelerometer sensitivity

(Y - voltage_ref)Y =

Accelerometer sensitivity

(Z - voltage_ref)Z =

Accelerometer sensitivit

Acc Acc Acc

Acc Acc Acc

Acc Acc Acc

y

Subtract tilt offset

X = (X - mean(X )) * 9.8

Y = (Y - mean(Y )) * 9.8

Z = (Z - mean(Z )) * 9.8


18

4.3 MATLAB Program

MATLAB was chosen to develop the activity monitoring algorithm because it

provides toolboxes for data analysis, serial communication, data visualization,

algorithm development and system models. Some of MATLAB’s toolboxes are signal

processing, neural networks, data visualization and data acquisition.

Initially, the user is prompted to enter the number of accelerometer samples. Once

the user specifies the number of samples, the program configures the serial port for

the XBee USB dongle which is connected to the USB port on the Laptop. COM 5 is

configured to have a baud rate of 57600, 8 data bits, 1 stop bit and no parity bit.

The serial port is opened so that a communication link can be established between

the wireless activity monitoring sensor and the laptop. The program receives the

accelerometer data from the sensor on COM 5 and stores the data into a CSV data

file. The accelerometer data is taken from the CSV file and filtered. Preprocessing

techniques (RMS and zero-crossing) are used to extract signal information from the

filtered accelerometer data.

A neural network model was created in MATLAB with the accelerometer training

data. The two features (zero crossing and peak RMS) are passed to the neural

network model and it determines the class (e.g. unoccupied chair, sitting etc). Once

the class has been identified, the neural network outputs two binary numbers that

represents the identified activity (sitting, getting up and unoccupied office chair). The

program displays the identified activity in the command window and it plots the three

axial accelerometer data (X, Y and Z). Figure 4.3 shows the accelerometer data for

the X, Y and Z axes.


19

Figure 4.3: Accelerometer Data

0 5 10 15-1

0

1A

cce

lera

tion

(m

2/s

)

Time (sec)


0 5 10 15-1

0

1

Acce

lera

tion

(m

2/s

)

Time (sec)


0 5 10 15-2

0

2

Acce

lera

tion

(m

2/s

)

Time (sec)



20

Chapter 5

5.0 Testing and Validation

This section discusses the testing and validation and it elaborates on the activity

detection, feature extraction and classification of the system. It analyzes the dataset

used and it discusses the accuracy of the system in identify human activity on an

office chair.

5.1 Data Description

The data used for training the Neural Network was collected while conducting

experimental tests on the office chair. The data was collected in a supervised

approach where the subject was given explicit instructions about what activity to

perform on the office chair (sitting or getting-up from the office chair). The data

corresponds to 5 subjects (3 women and 2 men) with different heights and weights.

Accelerometer data was recorded with a 15 second window and the subject would

perform the activity within this period. This data was then labelled with the performed

activity and it was used to build the activity classification model for the office chair.

The data was collected from a tri-axial accelerometer that was fixed to the back of

the office chair. The accelerometer was sampled at approximately 200 Hz. Figures

5.1.1, 5.1.2 and 5.1.3 illustrates the recorded activities.


21

Figure 5.1.1: Accelerometer Data for Unoccupied Office Chair

Figure 5.1.2: Accelerometer Data for Sitting Activity on Office Chair

0 5 10 15-0.01

0

0.01

0.02

g-f

orc

e

Time (sec)


0 5 10 15-0.01

-0.005

0

0.005

0.01

g-f

orc

e

Time (sec)


0 5 10 15-0.02

-0.01

0

0.01

g-f

orc

e

Time (sec)


0 5 10 15-0.1

0

0.1

0.2

0.3

g-f

orc

e

Time (sec)


0 5 10 15-0.2

-0.1

0

0.1

0.2

g-f

orc

e

Time (sec)


0 5 10 15-0.2

0

0.2

0.4

0.6

g-f

orc

e

Time (sec)



22

Figure 5.1.3: Accelerometer Data for Getting-up Activity on Office Chair

5.2 Feature Extraction

Once the raw data was recorded from the experimental tests, two features were

extracted from the data. They were zero-crossing and maximum root mean square

amplitude. Figure 5.2 illustrates the projection of these features onto a two

dimensional plot that explicitly shows the three classes (unoccupied, getting-up and

sitting) for human activity classification on an office chair . Clearly, it can be noticed

that the features for sitting and getting-up have very similar results which shows

some inaccuracies in distinguishing between the two activities. Table 5.2 shows the

experimental data set values for the feature extraction.

0 5 10 15-0.05

0

0.05

g-f

orc

e

Time (sec)


0 5 10 15-0.4

-0.2

0

0.2

0.4

g-f

orc

e

Time (sec)


0 5 10 15-0.2

0

0.2

0.4

0.6

g-f

orc

e

Time (sec)



23

Figure 5.2: Feature Extraction Data for Human Activity on an Office Chair

Class 1: Sitting Class 2: Getting up Class 3: Unoccupied

Zero-cross. Max RMS Zero-cross. Max RMS Zero-cross. Max RMS

23 0.52 19 0.42 221 0.041

15 1.08 27 0.94 220 0.041

13 0.94 17 0.40 204 0.048

11 1.172 25 0.40 201 0.042

11 1.87 19 0.66 174 0.043

11 0.77 28 0.54 203 0.0421

9 1.13 27 0.53 151 0.0432

9 1.69 21 0.81 237 0.0461

14 0.78 18 0.72 258 0.0512

18 0.98 17 0.54 204 0.0553

Table 5.2: Training Data Results

0 50 100 150 200 250 3000

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Ma

x R

MS

Am

plit

ud

e

Zero-crossing

Feature Extraction Data

Class 1: Sitting Down on Office Chair

Class 2: Getting-up from Office Chair

Class 3: Unoccupied Office Chair


24

5.3 Human Activity Classification Model for Office Chair

A neural network model was created using the training data results. Once the

network was trained, it was able to determine the best linear match for the three

classes (sitting, getting-up and unoccupied) based on the training data provided. The

linear lines in figure 5.3 represent the neural network model approximation. The

network model was trained using the MATLAB neural network toolbox (nntool).

In order to validate the classification model, it was very important to test the system

with measured real time data from the accelerometer. Subjects were given explicit

instructions about what activity to perform on the office chair and the classification

algorithm displayed the measured activity in the MATLAB command window. The

system had a 70 percent success rate using the neural network model. The neural

network model had trouble distinguishing between sitting and getting up on the office

chair because the feature extraction values were very similar for both activities.

To improve the success rate of the system, an additionally control parameter (min

and max RMS amplitude for each activity class) was added to the classification

algorithm to ensure the correct activity was identified. This enabled the system to

detect human activity on the office chair with a 90 percent success rate.

Figure 5.3: Feature Extraction Data with Linear Neural Network Model

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Chapter 6

6.0 Discussion

Due to similarities between different human activities (sitting and getting up) on the

office chair, the system was unable to identify the activities consistently (70%

accuracy). This may be due to the fact that the neural network was trained with a

small data set. In order to improve the system’s reliability, an additionally control

parameter (min and max RMS amplitude for each activity class) was added to the

classification algorithm. This improved the accuracy of the system to 90 percent.

The first issue that needs to be addressed in future work is distinguishing between

“sitting” and “getting-up” more precisely. This problem can be easily solved by

embedding a digital switch into the seat of the office chair. Another aspect that could

be improved is adding body posture among the classes being classified. This would

be advantageous for office chair ergonomics.

Main hardware concerns are related to obtaining a smaller size sensor, low power

consumption, low cost, high sensitivity and consistency of the system. Improvements

in low power consumptions for the wireless sensor were investigated but were not

implemented due to time restraints. The A/D converter on the microcontroller has a

unique feature of operating in sleep mode by using the conversion clock from the

A/D internal oscillator. This could be a useful feature for power saving when the

sensor is not active. This could reduce the overall power consumption for the sensor.


26

6.1 Conclusion

This project presented a prototype of a wireless activity monitoring system for data

acquisition and human activity classification. The prototype sensor, which uses an

accelerometer to measure human activity, is adaptable to household furnishings.

The proposed system aims to serve as a powerful tool for monitoring human activity

without attaching any sensors to the user. It allows data to be collected and analyzed

in a nonintrusive manner. In the preliminary tests, the system has proved to be

working with high accuracy and robustness. As the system has only used the

accelerometer data to derive a human activity classifier, future work may include the

use of other sensors (gyroscope and magnetic sensor) to determine other useful

data for human activity monitoring.

Ambient Activity Monitoring System Bibliography

27

Bibliography

1. D. W. McDonald, N.Schilt and J. Yang. Activity Recognition for the Digital Home.

2008.

2. Human Activity Recognition Using A single Tri-axil Accelerometer. Khan, Adil

Mehmood. Korea : Kyung Hee University, 2011.

3. Yamana, Himabindu. Embedded System for Monitoring Human Activities using

3-Axis Accelerometer. Texas : The University of Texas at Arlington, 2007.

4. Peixoto, Paulo, Batista, Jorge and Helder, J Araujo. Real-time Human Activity

Monitoring Exploring Multiple Vision Sensors. s.l. : IEEE, 2001.

5. An Activity Monitoring System for Real Elderly at Home: Validation Study. Zouba,

N. France : IEEE, 2010.

6. Activity Recognition in the Home using Simple Unbiquitous Sensors. E. M. Tapia,

S. S. Intille, and K Larson. Berlin/Heideberg : IEEE, 2004.

Ambient Activity Monitoring System Appendices

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Appendices

Appendix A – Activity Monitoring Sensor Schematic


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Appendix B – Microcontroller Program (dsPIC30F4011)


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Appendix C – MATLAB Program

% Written by Keith Connaughton and last updated 21/05/2013 % This function takes accelerometer data from the serial port and it stores % the data in a text file (.csv). It also plots the X,Y and Z axis data

from the % accelerometer. function Acc_Serial_Data()

clear all; clc;

% Load neural network model NN = load('nerualnetwork.mat');

% Initialise parameters samp_freq = 200; samp_interval = 1/samp_freq;

% Request the number of samples from user disp('Enter the number of samples to be taken from the serial port'); samples = input(''); SamPeriod = samples*samp_interval; disp(['Sampling period = ',num2str(SamPeriod),' seconds']);

% Create and open a text file for storing the data fid=fopen('AccData.csv', 'w+'); % Configure serial port ser = serial('com5'); ser.Terminator = 'CR'; ser.BaudRate = 57600; fopen(ser); fprintf(fid,'%8s %8s %8s %8s\n','T_Stamp','X','Y','Z');

% Take data in on serial port and store it to the text file for i = 1:samples accdata = fgetl(ser); data = sscanf(accdata,'\nX%dY%dZ%d'); b = all(size(data,1)==3); if b == true Time = timestamp(); fprintf(fid,'%8s %8d %8d %8d\n',Time,data(1),data(2),data(3)); end end

% Close serial port fclose(fid);

fclose(ser);delete(ser);clear ser;

fid = fopen('AccData.csv'); C = textscan(fid,'%8s %8s %8s %8s');


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%Format data into X, Y and Z (accelerometer axes) Xacc = C{1,2}; Yacc = C{1,3}; Zacc = C{1,4};

Xacc(1) = []; Yacc(1) = []; Zacc(1) = [];

Xacc = cellfun(@str2num,Xacc); Yacc = cellfun(@str2num,Yacc); Zacc = cellfun(@str2num,Zacc);

% Accelerometer sensitivity g_voltage_ref = 1.5; % 800mv/g accelerometer sensitivity sensitivity = 0.8;

% Data conversion Xvol = (Xacc.*3)./1024; Yvol = (Yacc.*3)./1024; Zvol = (Zacc.*3)./1024;

Xacc = (Xvol - g_voltage_ref)./sensitivity; Yacc = (Yvol - g_voltage_ref)./sensitivity; Zacc = (Zvol - g_voltage_ref)./sensitivity;

% Remove tilt offset from accelerometer data Xacc = Xacc - mean(Xacc); Yacc = Yacc - mean(Yacc); Zacc = Zacc - mean(Zacc);

% Root Mean Square calculation RMS = max(sqrt((Xacc.^2)+(Yacc.^2)+ (Zacc.^2)))

% Filter data (moving average) Xacc = smooth(Xacc,10,'moving'); Yacc = smooth(Yacc,10,'moving'); Zacc = smooth(Zacc,10,'moving');

% Zero crossing Xzero = zero_crossings(Xacc) Yzero = zero_crossings(Yacc) Zzero = zero_crossings(Zacc)

minzero = min([Xzero Yzero Zzero])

% Feature extraction data (RMS and zero-crossing) P = [minzero;RMS]

% Neural Network output result (activity classification) result = sim(NN.net,P)


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% Activity displayed to user if(result(1)==0 && result(2)==1) disp('User sat down on chair'); elseif(result(1)==1 && result(2)==1) disp('User got up from chair'); elseif(result(1)==1 && result(2)==0) disp('Unoccupied chair'); else disp('Unknown Activity'); end

% Plot accelerometer data tx = (1:size(Xacc)).* samp_interval; ty = (1:size(Yacc)).* samp_interval; tz = (1:size(Zacc)).* samp_interval;

subplot 311, plot(tx,Xacc), ylabel('G Force'),xlabel('Time (sec)'),

title('X Accelerometer Data'); subplot 312, plot(ty,Yacc),ylabel('G Force'),xlabel('Time (sec)'),

title('Y Accelerometer Data'); subplot 313, plot(tz,Zacc),ylabel('G Force'),xlabel('Time (sec)'),

title('Z Accelerometer Data');

end

function ts = timestamp() % % TIMESTAMP returns the current system time.

TempTime=clock; ts = [ num2str(TempTime(4),'%02.0f') ':' num2str(TempTime(5),'%02.0f') ':'

num2str(TempTime(6),'%02.0f')]; end


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Appendix D – Zero-crossing() MATLAB Function

function zerocount = zero_crossing(signal) % Count the number of zero-crossings from the time-domain % signal.

% initial value zerocount = 0;

signal = signal(:);

num_samp = length(signal); for i=2:num_samp

if((signal(i) * signal(i-1)) <= 0) zerocount = zerocount + 1; end

end


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Appendix E - Training Data Results

Figure E.1: Accelerometer Data for Unoccupied Office Chair


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Figure E.7: Accelerometer Data for Sitting Activity on Office Chair


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Figure E.13: Accelerometer Data for Getting-up Activity on Office Chair


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Ambient Activity Monitoring System

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