Journal
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
Volume 64, Issue 11, Pages 2575-2583Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2016.2641584
Keywords
Classifier feedback; electromyography; human-machine system; hand motion; prostheses; pattern recognition (PR); user training
Categories
Funding
- European Union [600915]
- National Natural Science Foundation of China [51575338, 51575407, 51475427]
- EPSRC [EP/G041377/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/G041377/1] Funding Source: researchfish
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It is evident that user training significantly affects performance of pattern-recognition-based myoelectric prosthetic device control. Despite plausible classification accuracy on offline datasets, online accuracy usually suffers from the changes in physiological conditions and electrode displacement. The user ability in generating consistent electromyographic (EMG) patterns can be enhanced via proper user training strategies in order to improve online performance. This study proposes a clustering-feedback strategy that provides real-time feedback to users by means of a visualized online EMG signal input as well as the centroids of the training samples, whose dimensionality is reduced to minimal number by dimension reduction. Clustering feedback provides a criterion that guides users to adjust motion gestures and muscle contraction forces intentionally. The experiment results have demonstrated that hand motion recognition accuracy increases steadily along the progress of the clustering-feedback-based user training, while conventional classifier-feedback methods, i.e., label feedback, hardly achieve any improvement. The result concludes that the use of proper classifier feedback can accelerate the process of user training, and implies prosperous future for the amputees with limited or no experience in pattern-recognition-based prosthetic device manipulation.
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