Dependence independence measure for posterior and anterior EMG sensors used in simple and complex finger flexion movements: Evaluation using SDICA

Ganesh R. Naik, Kerry G. Baker, Hung T. Nguyen

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

Identification of simple and complex finger flexion movements using surface electromyography (sEMG) and a muscle activation strategy is necessary to control human-computer interfaces such as prosthesis and orthoses. In order to identify these movements, sEMG sensors are placed on both anterior and posterior muscle compartments of the forearm. In general, the accuracy of myoelectric classification depends on several factors, which include number of sensors, features extraction methods, and classification algorithms. Myoelectric classification using a minimum number of sensors and optimal electrode configuration is always a challenging task. Sometimes, using several sensors including high density electrodes will not guarantee high classification accuracy. In this research, we investigated the dependence and independence nature of anterior and posterior muscles during simple and complex finger flexion movements. The outcome of this research shows that posterior parts of the hand muscles are dependent and hence responsible for most of simple finger flexion. On the other hand, this study shows that anterior muscles are responsible for most complex finger flexion. This also indicates that simple finger flexion can be identified using sEMG sensors connected only on anterior muscles (making posterior placement either independent or redundant), and vice versa is true for complex actions which can be easily identified using sEMG sensors on posterior muscles. The result of this study is beneficial for optimal electrode configuration and design of prosthetics and other related devices using a minimum number of sensors.

Original languageEnglish
Pages (from-to)1689-1696
Number of pages8
JournalIEEE Journal of Biomedical and Health Informatics
Volume19
Issue number5
DOIs
Publication statusPublished - 1 Sep 2015
Externally publishedYes

Keywords

  • Anterior
  • blind source separation (BSS)
  • posterior
  • simple and complex flexion
  • subband decomposition independent component analysis (ICA) (SDICA)
  • surface electromyography (sEMG)

Fingerprint Dive into the research topics of 'Dependence independence measure for posterior and anterior EMG sensors used in simple and complex finger flexion movements: Evaluation using SDICA'. Together they form a unique fingerprint.

Cite this