eprintid: 2462
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/24/62
datestamp: 2023-11-09 15:50:41
lastmod: 2023-11-09 15:50:41
status_changed: 2023-11-09 15:43:35
type: conference_item
metadata_visibility: show
creators_name: Khan, M.K.A.A.
creators_name: Parasuraman, S.
creators_name: Wei, T.C.
creators_name: Elamvazhuthi, I.
title: Genetic Algorithm for electromyography (EMG) and human locomotion
ispublished: pub
keywords: Biomechanical analysis; Development and applications; Electromyography (EMG); Functional tasks; Human lower extremity; Lower and upper limbs; Neurological disorders; Stroke rehabilitation, Biomechanics; Coordination reactions; Electrical engineering; Energy management; Genetic algorithms; Mathematical models; Patient rehabilitation; Patient treatment, Electromyography
note: cited By 1; Conference of International Conference on Emerging Trends in Electrical Engineering and Energy Management, ICETEEEM 2012 ; Conference Date: 13 December 2012 Through 15 December 2012; Conference Code:96684
abstract: The biomechanical analysis assists to provide evidences in the performance of the system used for stroke rehabilitation of lower and upper limb of human body. This could be done by providing a better understanding of human lower extremities movement through implementation of electromyography (EMG). As human body is a complex biomechanical machine, conducting analysis using only EMG is not sufficient in representing muscle coordination pattern for functional task (i.e. walking). For that, Genetic Algorithm (GA) is implemented in the selection process of best-fit mathematical model and its parameters used in conversion of EMG signal into estimated torque. Several experiments are conducted to validate the proposed method. The field of management and rehabilitation of motor disability is identified as one important application area. Based on relevant literature, the present paper asserts that scientific analysis of human movement patterns can materially affect patient treatment. It provides evidence that patient management and rehabilitation processes in central neurological disorders can be improved through EMG techniques. The use of electromyography for clinical planning in the treatment process of patients helps providing future directions in research, development and applications of scientific analysis of human movement. © 2012 IEEE.
date: 2012
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84876547235&doi=10.1109%2fICETEEEM.2012.6494497&partnerID=40&md5=bf6d15329cd43e935894528cb5d04249
id_number: 10.1109/ICETEEEM.2012.6494497
full_text_status: none
publication: Proceedings - ICETEEEM 2012, International Conference on Emerging Trends in Electrical Engineering and Energy Management
place_of_pub: Chennai, Tamil Nadu
pagerange: 276-282
refereed: TRUE
isbn: 9781467346337
citation:   Khan, M.K.A.A. and Parasuraman, S. and Wei, T.C. and Elamvazhuthi, I.  (2012) Genetic Algorithm for electromyography (EMG) and human locomotion.  In: UNSPECIFIED.