Development of paraplegic quadriceps muscle models using artificial intelligent techniques / Syafinas Mohd Salleh

This research presents the development of quadriceps muscle models using several Artificial Intelligent techniques. The data of paraplegic muscle behavior consists of frequency, sampling time, pulse width and muscle torque have been obtained from Hospital Sungai Buloh. The data was collected from 20...

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Main Author:	Mohd Salleh, Syafinas
Format:	Thesis
Language:	English
Published:	2015
Subjects:	Artificial immune systems Immunocomputers
Online Access:	https://ir.uitm.edu.my/id/eprint/15276/1/TM_SYAFINAS%20MOHD%20SALLEH%20EE%2015_5.pdf
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id	my-uitm-ir.15276
record_format	uketd_dc
spelling	my-uitm-ir.152762022-03-09T07:42:29Z Development of paraplegic quadriceps muscle models using artificial intelligent techniques / Syafinas Mohd Salleh 2015 Mohd Salleh, Syafinas Artificial immune systems. Immunocomputers This research presents the development of quadriceps muscle models using several Artificial Intelligent techniques. The data of paraplegic muscle behavior consists of frequency, sampling time, pulse width and muscle torque have been obtained from Hospital Sungai Buloh. The data was collected from 20 paraplegic patient’s age between 40-60 years olds throughout the study. 722 data obtained from the data collection to be used for muscle model development. In this study Artificial Neural Network (ANN) will be used to develop paraplegic muscle models. There are Levenberg Marquardt (LM),Resilient Backpropagation (RP) and Scale Conjugate Gradient (SCG). Other AI techniques involve in this study are Fuzzy Logic and Adaptive Neural Fuzzy Interference System (ANFIS). The last technique that has been selected for the muscle model is Extended Kalman Filter (EKF) techniques. From the result, LM gives the best performance compare to others model developed. From analysis for all model develops, LM gives the lowest Mean Squared Error (MSE) 0.2007 followed by RP with MSE 0.2644, SCG 0.3758, Fuzzy Logic 0.7557, ANFIS 1.36 and EKF 29.68. In this analysis,the proposed EKF shows unreliable results where it produces the highest error compared to the other techniques. It shows that EKF is not suitable to be used for develop quadriceps muscle model with unsynchronized type of data behavior. The result obtained in this study can be used in the future for design and evaluation of various control strategies that could help avoid injuries during the experiment on real paraplegic patients 2015 Thesis https://ir.uitm.edu.my/id/eprint/15276/ https://ir.uitm.edu.my/id/eprint/15276/1/TM_SYAFINAS%20MOHD%20SALLEH%20EE%2015_5.pdf text en public mphil masters Universiti Teknologi MARA Faculty Electrical Engineering
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
topic	Artificial immune systems Immunocomputers
spellingShingle	Artificial immune systems Immunocomputers Mohd Salleh, Syafinas Development of paraplegic quadriceps muscle models using artificial intelligent techniques / Syafinas Mohd Salleh
description	This research presents the development of quadriceps muscle models using several Artificial Intelligent techniques. The data of paraplegic muscle behavior consists of frequency, sampling time, pulse width and muscle torque have been obtained from Hospital Sungai Buloh. The data was collected from 20 paraplegic patient’s age between 40-60 years olds throughout the study. 722 data obtained from the data collection to be used for muscle model development. In this study Artificial Neural Network (ANN) will be used to develop paraplegic muscle models. There are Levenberg Marquardt (LM),Resilient Backpropagation (RP) and Scale Conjugate Gradient (SCG). Other AI techniques involve in this study are Fuzzy Logic and Adaptive Neural Fuzzy Interference System (ANFIS). The last technique that has been selected for the muscle model is Extended Kalman Filter (EKF) techniques. From the result, LM gives the best performance compare to others model developed. From analysis for all model develops, LM gives the lowest Mean Squared Error (MSE) 0.2007 followed by RP with MSE 0.2644, SCG 0.3758, Fuzzy Logic 0.7557, ANFIS 1.36 and EKF 29.68. In this analysis,the proposed EKF shows unreliable results where it produces the highest error compared to the other techniques. It shows that EKF is not suitable to be used for develop quadriceps muscle model with unsynchronized type of data behavior. The result obtained in this study can be used in the future for design and evaluation of various control strategies that could help avoid injuries during the experiment on real paraplegic patients
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Mohd Salleh, Syafinas
author_facet	Mohd Salleh, Syafinas
author_sort	Mohd Salleh, Syafinas
title	Development of paraplegic quadriceps muscle models using artificial intelligent techniques / Syafinas Mohd Salleh
title_short	Development of paraplegic quadriceps muscle models using artificial intelligent techniques / Syafinas Mohd Salleh
title_full	Development of paraplegic quadriceps muscle models using artificial intelligent techniques / Syafinas Mohd Salleh
title_fullStr	Development of paraplegic quadriceps muscle models using artificial intelligent techniques / Syafinas Mohd Salleh
title_full_unstemmed	Development of paraplegic quadriceps muscle models using artificial intelligent techniques / Syafinas Mohd Salleh
title_sort	development of paraplegic quadriceps muscle models using artificial intelligent techniques / syafinas mohd salleh
granting_institution	Universiti Teknologi MARA
granting_department	Faculty Electrical Engineering
publishDate	2015
url	https://ir.uitm.edu.my/id/eprint/15276/1/TM_SYAFINAS%20MOHD%20SALLEH%20EE%2015_5.pdf
_version_	1783733364423917568

Development of paraplegic quadriceps muscle models using artificial intelligent techniques / Syafinas Mohd Salleh

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