Limb-position robust classification of myoelectric signals for prosthesis control using sparse representations

Joseph L. Betthauser, Christopher L. Hunt, Luke E. Osborn, Rahul R. Kaliki, Nitish V Thakor

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

Abstract

The fundamental objective in non-invasive myoelectric prosthesis control is to determine the user's intended movements from corresponding skin-surface recorded electromyographic (sEMG) activation signals as quickly and accurately as possible. Linear Discriminant Analysis (LDA) has emerged as the de facto standard for real-time movement classification due to its ease of use, calculation speed, and remarkable classification accuracy under controlled training conditions. However, performance of cluster-based methods like LDA for sEMG pattern recognition degrades significantly when real-world testing conditions do not resemble the trained conditions, limiting the utility of myoelectrically controlled prosthesis devices. We propose an enhanced classification method that is more robust to generic deviations from training conditions by constructing sparse representations of the input data dictionary comprised of sEMG time-frequency features. We apply our method in the context of upper-limb position changes to demonstrate pattern recognition robustness and improvement over LDA across discrete positions not explicitly trained. For single position training we report an accuracy improvement in untrained positions of 7.95%, p ≪.001, in addition to significant accuracy improvements across all multiposition training conditions, p <.001.

Original languageEnglish (US)
Title of host publication2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6373-6376
Number of pages4
Volume2016-October
ISBN (Electronic)9781457702204
DOIs
StatePublished - Oct 13 2016
Event38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 - Orlando, United States
Duration: Aug 16 2016Aug 20 2016

Other

Other38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
CountryUnited States
CityOrlando
Period8/16/168/20/16

Keywords

  • Amputee
  • Classification
  • Clinical need
  • Limb-position effect
  • Myoelectric control
  • Non-invasive
  • Pattern recognition
  • Robust
  • SFT1
  • Sparse representation
  • Upper-limb prosthesis

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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  • Cite this

    Betthauser, J. L., Hunt, C. L., Osborn, L. E., Kaliki, R. R., & Thakor, N. V. (2016). Limb-position robust classification of myoelectric signals for prosthesis control using sparse representations. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 (Vol. 2016-October, pp. 6373-6376). [7592186] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2016.7592186