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Sensors 2017, 17(6), 1370; doi:10.3390/s17061370

A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG) Pattern Recognition

1
State Key Laboratory of Robotics and System, School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China
2
Research Center for Advanced Science and Technology, the University of Tokyo and PRESTO/JST, Tokyo 153-8904, Japan
*
Authors to whom correspondence should be addressed.
Received: 15 April 2017 / Revised: 5 June 2017 / Accepted: 8 June 2017 / Published: 13 June 2017
(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)

Abstract

Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents a particle adaptive classifier (PAC), by constructing a particle adaptive learning strategy and universal incremental least square support vector classifier (LS-SVC). We compared PAC performance with incremental support vector classifier (ISVC) and non-adapting SVC (NSVC) in a long-term pattern recognition task in both unsupervised and supervised adaptive learning scenarios. Retraining time cost and recognition accuracy were compared by validating the classification performance on both simulated and realistic long-term EMG data. The classification results of realistic long-term EMG data showed that the PAC significantly decreased the performance degradation in unsupervised adaptive learning scenarios compared with NSVC (9.03% ± 2.23%, p < 0.05) and ISVC (13.38% ± 2.62%, p = 0.001), and reduced the retraining time cost compared with ISVC (2 ms per updating cycle vs. 50 ms per updating cycle). View Full-Text
Keywords: long-term EMG pattern recognition; adaptive learning; concept drift; particle adaption; support vector classifier long-term EMG pattern recognition; adaptive learning; concept drift; particle adaption; support vector classifier
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Huang, Q.; Yang, D.; Jiang, L.; Zhang, H.; Liu, H.; Kotani, K. A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG) Pattern Recognition. Sensors 2017, 17, 1370.

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