Estimating the instantaneous wrist flexion angle from multi-channel surface EMG of forearm muscles

Bence J. Borbély, Péter Szolgay

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

3 Citations (Scopus)

Abstract

A pattern recognition based classification method is proposed to estimate wrist flexion angles from electrical activities of forearm muscles. Spatial movement data and multi-channel myoelectric signals from forearm muscles were collected experimentally during periodic wrist flexion and extension movements using an ultrasound based movement analyser system. The recorded marker coordinates were transformed into joint angles using OpenSim, an open source simulation tool for biomechanical analysis. EMG data were segmented according to specific ranges of the calculated wrist flexion angle to form different classes for pattern recognition. The parameter space of the used classification algorithm was explored with a selected subset of values to find the optimal parameter vector giving maximal classification performance.

Original languageEnglish
Title of host publication2013 IEEE Biomedical Circuits and Systems Conference, BioCAS 2013
Pages77-80
Number of pages4
DOIs
Publication statusPublished - Dec 1 2013
Event2013 IEEE Biomedical Circuits and Systems Conference, BioCAS 2013 - Rotterdam, Netherlands
Duration: Oct 31 2013Nov 2 2013

Publication series

Name2013 IEEE Biomedical Circuits and Systems Conference, BioCAS 2013

Other

Other2013 IEEE Biomedical Circuits and Systems Conference, BioCAS 2013
CountryNetherlands
CityRotterdam
Period10/31/1311/2/13

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ASJC Scopus subject areas

  • Hardware and Architecture
  • Biomedical Engineering
  • Electrical and Electronic Engineering

Cite this

Borbély, B. J., & Szolgay, P. (2013). Estimating the instantaneous wrist flexion angle from multi-channel surface EMG of forearm muscles. In 2013 IEEE Biomedical Circuits and Systems Conference, BioCAS 2013 (pp. 77-80). [6679643] (2013 IEEE Biomedical Circuits and Systems Conference, BioCAS 2013). https://doi.org/10.1109/BioCAS.2013.6679643