Classification of ankle joint movements based on surface electromyography signals

Electromyography (EMG) signal has valuable information about the force of the muscle contraction and the movement direction. This crucial information has been used for many years in exoskeleton, orthoses and prostheses robots. An essential part of those devices is EMG based control system that emplo...

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Main Author:	Al-Quraishi, Maged Saleh Saeed
Format:	Thesis
Language:	English
Published:	2015
Subjects:	Electromyography Muscles Ankle
Online Access:	http://psasir.upm.edu.my/id/eprint/56615/1/FK%202015%2023RR.pdf
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spelling	my-upm-ir.566152017-07-31T04:44:20Z Classification of ankle joint movements based on surface electromyography signals 2015-08 Al-Quraishi, Maged Saleh Saeed Electromyography (EMG) signal has valuable information about the force of the muscle contraction and the movement direction. This crucial information has been used for many years in exoskeleton, orthoses and prostheses robots. An essential part of those devices is EMG based control system that employs the EMG signal from different muscles to control prostheses and exoskeleton robot. However, using EMG signal as an input control signal for those devices is not easy due to the complexity nature of this signal that produces the different body movements. This difficulty can be overcome by using pattern recognition techniques to discriminant different limb movement’s pattern then use the classified signal as input control signal to manipulate and drive the assistive robot devices. Though much research have been carried out to classify the upper and lower limbs movement based on the EMG signal, still there is a strong need to obtain an accurate pattern classification system in computationally efficient manner. In this work two parts are primarily presented. The first partt was design and implements a multichannel EMG acquisition system to detect and acquire the leg muscles’ signal. In this part four EMG channels were implemented using instrumentation amplifier (INA114) for pre-amplification stage then the amplified signal was filtered using band pass filter to eliminate the unwanted signals. Operational amplifier (OPA2604) was involved for the main amplification stage to get the output signal in volts. The EMG signals were detected during movement of the ankle joint of a healthy subjects. Then the signal sampled at rate of 2 kHz using NI 6009 DAQ card and LabVIEW software was employed to store and display the acquired signal. Fast Fourier Transform (FFT) and Signal to Noise Ratio (SNR) were applied to assess the recoded electromyography signal. The second part is to classify four ankle joint movements which are dorsiflexion, plantar flexion, adduction and abduction. The data was collected from twenty healthy subjects using the custom multichannel EMG acquisition system designed in the first part of this project. In this section, new time domain feature set was evaluated and compared with well known time domain features. Three classifiers were employed to evaluate the two feature sets. These classifiers are linear discriminant Analysis (LDA), K nearest neighbourhood (k-NN) and Naïve Bayes classifier (NB). The result showed the superiority of the new time domain feature set which are the logarithmic based time domain features upon the conventional time domain feature. In addition, the results show the outperformance of LDA classifier among the other two classifiers used in this study. Electromyography Muscles Ankle 2015-08 Thesis http://psasir.upm.edu.my/id/eprint/56615/ http://psasir.upm.edu.my/id/eprint/56615/1/FK%202015%2023RR.pdf application/pdf en public masters Universiti Putra Malaysia Electromyography Muscles Ankle
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Electromyography Muscles Ankle
spellingShingle	Electromyography Muscles Ankle Al-Quraishi, Maged Saleh Saeed Classification of ankle joint movements based on surface electromyography signals
description	Electromyography (EMG) signal has valuable information about the force of the muscle contraction and the movement direction. This crucial information has been used for many years in exoskeleton, orthoses and prostheses robots. An essential part of those devices is EMG based control system that employs the EMG signal from different muscles to control prostheses and exoskeleton robot. However, using EMG signal as an input control signal for those devices is not easy due to the complexity nature of this signal that produces the different body movements. This difficulty can be overcome by using pattern recognition techniques to discriminant different limb movement’s pattern then use the classified signal as input control signal to manipulate and drive the assistive robot devices. Though much research have been carried out to classify the upper and lower limbs movement based on the EMG signal, still there is a strong need to obtain an accurate pattern classification system in computationally efficient manner. In this work two parts are primarily presented. The first partt was design and implements a multichannel EMG acquisition system to detect and acquire the leg muscles’ signal. In this part four EMG channels were implemented using instrumentation amplifier (INA114) for pre-amplification stage then the amplified signal was filtered using band pass filter to eliminate the unwanted signals. Operational amplifier (OPA2604) was involved for the main amplification stage to get the output signal in volts. The EMG signals were detected during movement of the ankle joint of a healthy subjects. Then the signal sampled at rate of 2 kHz using NI 6009 DAQ card and LabVIEW software was employed to store and display the acquired signal. Fast Fourier Transform (FFT) and Signal to Noise Ratio (SNR) were applied to assess the recoded electromyography signal. The second part is to classify four ankle joint movements which are dorsiflexion, plantar flexion, adduction and abduction. The data was collected from twenty healthy subjects using the custom multichannel EMG acquisition system designed in the first part of this project. In this section, new time domain feature set was evaluated and compared with well known time domain features. Three classifiers were employed to evaluate the two feature sets. These classifiers are linear discriminant Analysis (LDA), K nearest neighbourhood (k-NN) and Naïve Bayes classifier (NB). The result showed the superiority of the new time domain feature set which are the logarithmic based time domain features upon the conventional time domain feature. In addition, the results show the outperformance of LDA classifier among the other two classifiers used in this study.
format	Thesis
qualification_level	Master's degree
author	Al-Quraishi, Maged Saleh Saeed
author_facet	Al-Quraishi, Maged Saleh Saeed
author_sort	Al-Quraishi, Maged Saleh Saeed
title	Classification of ankle joint movements based on surface electromyography signals
title_short	Classification of ankle joint movements based on surface electromyography signals
title_full	Classification of ankle joint movements based on surface electromyography signals
title_fullStr	Classification of ankle joint movements based on surface electromyography signals
title_full_unstemmed	Classification of ankle joint movements based on surface electromyography signals
title_sort	classification of ankle joint movements based on surface electromyography signals
granting_institution	Universiti Putra Malaysia
publishDate	2015
url	http://psasir.upm.edu.my/id/eprint/56615/1/FK%202015%2023RR.pdf
_version_	1747812133604687872

Classification of ankle joint movements based on surface electromyography signals

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