JOINT TORQUEESTIMATION OF sEMG SIGNALS BASED ON INTELLIGENT TECHNIQUES FOR ROBOTIC ASSISTIVE SYSTEM

Stroke is the leading cause of disability of people that influences the quality of their daily life where an effective method is required for post-stroke rehabilitation. Research has shown that robot is a good alternative where the electromyography (EMG) signals have been proposed as an input to...

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Main Author: KU ABD RAHIM, KU NURHANIM
Format: Thesis
Language: English
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-utpedia.21123 /
Published: 2014
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access: http://utpedia.utp.edu.my/21123/1/2014%20-%20ELECTRICAL%20-%20JOINT%20TORQUEESTIMATION%20OF%20sEMG%20SIGNALS%20BASED%20ON%20INTELLIGENT%20TECHNIQUES%20FOR%20ROBOTIC%20ASSISTIVE%20SYSTEM%20-%20KU%20NURHANIM%20BT%20KU%20ABD%20RAHIM.pdf
http://utpedia.utp.edu.my/21123/
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spelling utp-utpedia.211232021-09-15T20:08:48Z http://utpedia.utp.edu.my/21123/ JOINT TORQUEESTIMATION OF sEMG SIGNALS BASED ON INTELLIGENT TECHNIQUES FOR ROBOTIC ASSISTIVE SYSTEM KU ABD RAHIM, KU NURHANIM TK Electrical engineering. Electronics Nuclear engineering Stroke is the leading cause of disability of people that influences the quality of their daily life where an effective method is required for post-stroke rehabilitation. Research has shown that robot is a good alternative where the electromyography (EMG) signals have been proposed as an input to control the jointtorque of robotic assistive system for rehabilitation. This research focuses on implementation of intelligent algorithms to convert the sEMG signals tojoint torque for task movement ofelbow flexion and knee extension for robotic assistive system. In order to achieve this, several investigations were carried out. Initially, experimental for sEMG signals acquisition from different test subjects was conducted using a wireless EMG system. Subsequently, the signal processing that involved processes such as filtering and feature extraction was conducted. Thereafter, the actual joint torque was acquired through dynamic modeling. Finally, simulation was conducted on nine mathematical models to convert the sEMG signals to estimated joint torque using optimization techniques such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). 2014-01 Thesis NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/21123/1/2014%20-%20ELECTRICAL%20-%20JOINT%20TORQUEESTIMATION%20OF%20sEMG%20SIGNALS%20BASED%20ON%20INTELLIGENT%20TECHNIQUES%20FOR%20ROBOTIC%20ASSISTIVE%20SYSTEM%20-%20KU%20NURHANIM%20BT%20KU%20ABD%20RAHIM.pdf KU ABD RAHIM, KU NURHANIM (2014) JOINT TORQUEESTIMATION OF sEMG SIGNALS BASED ON INTELLIGENT TECHNIQUES FOR ROBOTIC ASSISTIVE SYSTEM. Masters thesis, Universiti Teknologi PETRONAS.
institution Universiti Teknologi Petronas
collection UTPedia
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
KU ABD RAHIM, KU NURHANIM
JOINT TORQUEESTIMATION OF sEMG SIGNALS BASED ON INTELLIGENT TECHNIQUES FOR ROBOTIC ASSISTIVE SYSTEM
description Stroke is the leading cause of disability of people that influences the quality of their daily life where an effective method is required for post-stroke rehabilitation. Research has shown that robot is a good alternative where the electromyography (EMG) signals have been proposed as an input to control the jointtorque of robotic assistive system for rehabilitation. This research focuses on implementation of intelligent algorithms to convert the sEMG signals tojoint torque for task movement ofelbow flexion and knee extension for robotic assistive system. In order to achieve this, several investigations were carried out. Initially, experimental for sEMG signals acquisition from different test subjects was conducted using a wireless EMG system. Subsequently, the signal processing that involved processes such as filtering and feature extraction was conducted. Thereafter, the actual joint torque was acquired through dynamic modeling. Finally, simulation was conducted on nine mathematical models to convert the sEMG signals to estimated joint torque using optimization techniques such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).
format Thesis
author KU ABD RAHIM, KU NURHANIM
author_sort KU ABD RAHIM, KU NURHANIM
title JOINT TORQUEESTIMATION OF sEMG SIGNALS BASED ON INTELLIGENT TECHNIQUES FOR ROBOTIC ASSISTIVE SYSTEM
title_short JOINT TORQUEESTIMATION OF sEMG SIGNALS BASED ON INTELLIGENT TECHNIQUES FOR ROBOTIC ASSISTIVE SYSTEM
title_full JOINT TORQUEESTIMATION OF sEMG SIGNALS BASED ON INTELLIGENT TECHNIQUES FOR ROBOTIC ASSISTIVE SYSTEM
title_fullStr JOINT TORQUEESTIMATION OF sEMG SIGNALS BASED ON INTELLIGENT TECHNIQUES FOR ROBOTIC ASSISTIVE SYSTEM
title_full_unstemmed JOINT TORQUEESTIMATION OF sEMG SIGNALS BASED ON INTELLIGENT TECHNIQUES FOR ROBOTIC ASSISTIVE SYSTEM
title_sort joint torqueestimation of semg signals based on intelligent techniques for robotic assistive system
publishDate 2014
url http://utpedia.utp.edu.my/21123/1/2014%20-%20ELECTRICAL%20-%20JOINT%20TORQUEESTIMATION%20OF%20sEMG%20SIGNALS%20BASED%20ON%20INTELLIGENT%20TECHNIQUES%20FOR%20ROBOTIC%20ASSISTIVE%20SYSTEM%20-%20KU%20NURHANIM%20BT%20KU%20ABD%20RAHIM.pdf
http://utpedia.utp.edu.my/21123/
_version_ 1741195708087664640
score 11.62408

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