Joint torque estimation model of surface electromyography(sEMG) based on swarm intelligence algorithm for robotic assistive device

Ku Nurhanim, I. Elamvazuthi, P. Vasant, T. Ganesan, S. Parasuraman, M. K.A. Ahamed Khan

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

7 Citations (Scopus)


The conventional robotic assistive device was based on pre-programmed functions by the robot expert. This makes it difficult for stroke patients use it effectively due to difficulty of torque setting that is suitable for the user movement. Electromyography (EMG) signal measures the electrical signal of muscle contraction. The EMG-based robotics assistive technology would enable the stroke patients to control the robot movement according to the user's own strength of natural movement. This paper discusses the mapping of surface electromyography signals (sEMG) to torque for robotic rehabilitation. Particle swarm optimization (PSO) has been applied as a control algorithm for a number of selected mathematical models. sEMG signals were determined as input data to the mathematical model where parameters of the mathematical model were optimized using PSO. Hence, the good correlated estimated torque as output was obtained.

Original languageEnglish
Title of host publicationInternational Symposium on Medical and Rehabilitation Robotics and Instrumentation, MRRI 2013
Number of pages8
Publication statusPublished - 2014
EventInternational Symposium on Medical and Rehabilitation Robotics and Instrumentation 2013 - Shah Alam, Malaysia
Duration: 2 Dec 20134 Dec 2013 (Proceedings)

Publication series

NameProcedia Computer Science
ISSN (Print)1877-0509


ConferenceInternational Symposium on Medical and Rehabilitation Robotics and Instrumentation 2013
Abbreviated titleMRRI 2013
CityShah Alam
Internet address


  • Biomechanics human motion
  • Electromyography
  • Feature extraction
  • Joint torque estimation model
  • Particle swarm optimization
  • Robot rehabilitation

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