Improving the robustness of myoelectric pattern recognition for upper limb prostheses by covariate shift adaptation

Marina M.C. Vidovic, Han Jeong Hwang, Sebastian Amsuss, Janne M. Hahne, Dario Farina, Klaus Robert Muller

Research output: Contribution to journalArticlepeer-review

121 Citations (Scopus)


Fundamental changes over time of surface EMG signal characteristics are a challenge for myocontrol algorithms controlling prosthetic devices. These changes are generally caused by electrode shifts after donning and doffing, sweating, additional weight or varying arm positions, which results in a change of the signal distribution - a scenario often referred to as covariate shift. A substantial decrease in classification accuracy due to these factors hinders the possibility to directly translate EMG signals into accurate myoelectric control patterns outside laboratory conditions. To overcome this limitation, we propose the use of supervised adaptation methods. The approach is based on adapting a trained classifier using a small calibration set only, which incorporates the relevant aspects of the nonstationarities, but requires only less than 1 min of data recording. The method was tested first through an offline analysis on signals acquired across 5 days from seven able-bodied individuals and four amputees. Moreover, we also conducted a three day online experiment on eight able-bodied individuals and one amputee, assessing user performance and user-ratings of the controllability. Across different testing days, both offline and online performance improved significantly when shrinking the training model parameters by a given estimator towards the calibration set parameters. In the offline data analysis, the classification accuracy remained above 92% over five days with the proposed approach, whereas it decreased to 75% without adaptation. Similarly, in the online study, with the proposed approach the performance increased by 25% compared to a test without adaptation. These results indicate that the proposed methodology can contribute to improve robustness of myoelectric pattern recognition methods in daily life applications.

Original languageEnglish
Article number7302056
Pages (from-to)961-970
Number of pages10
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Issue number9
Publication statusPublished - 2016 Sept


  • Adaptation
  • classification
  • covariate shift
  • electromyogram (EMG)
  • myoelectric signals
  • nonstationarities
  • pattern recognition

ASJC Scopus subject areas

  • Internal Medicine
  • Neuroscience(all)
  • Biomedical Engineering
  • Rehabilitation


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