Classification of Finger Movements for the Dexterous Hand Prosthesis Control With Surface Electromyography

Ali H. Al-Timemy*, Guido Bugmann, Javier Escudero, Nicholas Outram

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

A method for the classification of finger movements for dexterous control of prosthetic hands is proposed. Previous research was mainly devoted to identify hand movements as these actions generate strong electromyography (EMG) signals recorded from the forearm. In contrast, in this paper, we assess the use of multichannel surface electromyography (sEMG) to classify individual and combined finger movements for dexterous prosthetic control. sEMG channels were recorded from ten intact-limbed and six below-elbow amputee persons. Offline processing was used to evaluate the classification performance. The results show that high classification accuracies can be achieved with a processing chain consisting of time domain-autoregression feature extraction, orthogonal fuzzy neighborhood discriminant analysis for feature reduction, and linear discriminant analysis for classification. We show that finger and thumb movements can be decoded accurately with high accuracy with latencies as short as 200 ms. Thumb abduction was decoded successfully with high accuracy for six amputee persons for the first time. We also found that subsets of six EMG channels provide accuracy values similar to those computed with the full set of EMG channels (98% accuracy over ten intact-limbed subjects for the classification of 15 classes of different finger movements and 90% accuracy over six amputee persons for the classification of 12 classes of individual finger movements). These accuracy values are higher than previous studies, whereas we typically employed half the number of EMG channels per identified movement.

Original languageEnglish
Pages (from-to)608-618
Number of pages11
JournalIEEE Journal of Biomedical and Health Informatics
Volume17
Issue number3
DOIs
Publication statusPublished - May 2013

Keywords / Materials (for Non-textual outputs)

  • Electromyography
  • linear discriminant analysis (LDA)
  • pattern recognition
  • prosthetic hand
  • PATTERN-RECOGNITION
  • TRANSRADIAL AMPUTEES
  • MYOELECTRIC CONTROL
  • CHALLENGES
  • SIGNAL

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