eprintid: 8848
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/88/48
datestamp: 2023-11-09 16:20:46
lastmod: 2023-11-09 16:20:46
status_changed: 2023-11-09 16:13:41
type: conference_item
metadata_visibility: show
creators_name: Nurhanim, K.
creators_name: Elamvazuthi, I.
creators_name: Izhar, L.I.
creators_name: Ganesan, T.
creators_name: Su, S.W.
title: Development of a model for sEMG based joint-torque estimation using Swarm techniques
ispublished: pub
keywords: Electromyography; Estimation; Joints (anatomy); Manufacture; Mathematical transformations; Robotics; Torque; Traffic signals, Coefficient of determination; Joint torques; Knee extension; Rehabilitation robot; Research focus; Sum squared error; Surface electromyography; Swarm techniques, Particle swarm optimization (PSO)
note: cited By 4; Conference of 2nd IEEE International Symposium on Robotics and Manufacturing Automation, ROMA 2016 ; Conference Date: 25 September 2016 Through 27 September 2016; Conference Code:126431
abstract: Over the years, numerous researchers have explored the relationship between surface electromyography (sEMG) signal with joint torque that would be useful to develop a suitable controller for rehabilitation robot. This research focuses on the transformation of sEMG signal by adopting a mathematical model to find the estimated joint torque of knee extension. Swarm techniques such as Particle Swarm Optimization (PSO) and Improved Particle Swarm Optimization (IPSO) were adapted to optimize the mathematical model for estimated joint torque. The correlation between the estimated joint torque and actual joint torque were determined by Coefficient of Determination (R2) and fitness value of Sum Squared Error (SSE). The outcome of the research shows that both the PSO and IPSO have yielded promising results. © 2016 IEEE.
date: 2017
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015922961&doi=10.1109%2fROMA.2016.7847833&partnerID=40&md5=0e20efc222787d3f690f600731ce8840
id_number: 10.1109/ROMA.2016.7847833
full_text_status: none
publication: 2016 2nd IEEE International Symposium on Robotics and Manufacturing Automation, ROMA 2016
refereed: TRUE
isbn: 9781509009282
citation:   Nurhanim, K. and Elamvazuthi, I. and Izhar, L.I. and Ganesan, T. and Su, S.W.  (2017) Development of a model for sEMG based joint-torque estimation using Swarm techniques.  In: UNSPECIFIED.