Detection of and Compensation for EMG Disturbances for Powered Lower Limb Prosthesis Control

Research output: Contribution to journalArticle

30 Scopus citations

Abstract

Myoelectric pattern recognition algorithms have been proposed for the control of powered lower limb prostheses, but electromyography (EMG) signal disturbances remain an obstacle to clinical implementation. To address this problem, we used a log-likelihood metric to detect simulated EMG disturbances and real disturbances acquired from EMG containing electrode shift. We found that features extracted from disturbed EMG have much lower log likelihoods than those from undisturbed signals and can be detected using a single threshold acquired from the training data. We designed a linear discriminant analysis (LDA) classifier that uses the log likelihood to decide between using a combination of EMG and mechanical sensors and using mechanical sensors only, to predict locomotion modes. When EMG contained disturbances, our classifier detected those disturbances and disregarded EMG data. Our classifier had significantly lower errors than a standard LDA classifier in the presence of EMG disturbances. The log-likelihood classifier had a low false positive threshold, and thus did not perform significantly differently from the standard LDA classifier when EMG did not contain disturbances. The log-likelihood threshold could also be applied to individual EMG channels, enabling specific channels containing EMG disturbances to be appropriately ignored when making locomotion mode predictions.

Original languageEnglish (US)
Article number7070694
Pages (from-to)226-234
Number of pages9
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume24
Issue number2
DOIs
StatePublished - Feb 1 2016

Keywords

  • Adaptive algorithms
  • electromyography (EMG)
  • neural engineering
  • pattern recognition
  • powered prostheses

ASJC Scopus subject areas

  • Neuroscience(all)
  • Biomedical Engineering
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Detection of and Compensation for EMG Disturbances for Powered Lower Limb Prosthesis Control'. Together they form a unique fingerprint.

  • Cite this