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Adaptive Hybrid Classifier for Myoelectric Pattern Recognition Against the Interferences of Outlier Motion, Muscle Fatigue, and Electrode Doffing
- Source :
- IEEE Transactions on Neural Systems and Rehabilitation Engineering. 27:1071-1080
- Publication Year :
- 2019
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2019.
-
Abstract
- Traditional myoelectric prostheses that employ a static pattern recognition model to identify human movement intention from surface electromyography (sEMG) signals hardly adapt to the changes in the sEMG characteristics caused by interferences from daily activities, which hinders the clinical applications of such prostheses. In this paper, we focus on methods to reduce or eliminate the impacts of three types of daily interferences on myoelectric pattern recognition (MPR), i.e., outlier motion, muscle fatigue, and electrode doffing/donning. We constructed an adaptive incremental hybrid classifier (AIHC) by combining one-class support vector data description and multi-class linear discriminant analysis in conjunction with two specific update schemes. We developed an AIHC-based MPR strategy to improve the robustness of MPR against the three interferences. Extensive experiments on hand-motion recognition were conducted to demonstrate the performance of the proposed method. Experimental results show that the AIHC has significant advantages over non-adaptive classifiers under various interferences, with improvements in the classification accuracy ranging from 7.1% to 39% ( ${p} ). The additional evaluations on data deviations demonstrate that the AIHC can accommodate large-scale changes in the sEMG characteristics, revealing the potential of the AIHC-based MPR strategy in the development of clinical myoelectric prostheses.
- Subjects :
- Adult
Male
Support Vector Machine
Computer science
0206 medical engineering
Biomedical Engineering
02 engineering and technology
Prosthesis Design
Data description
Pattern Recognition, Automated
Motion
Young Adult
Robustness (computer science)
0202 electrical engineering, electronic engineering, information engineering
Internal Medicine
Humans
Electrodes
Muscle fatigue
Electromyography
business.industry
General Neuroscience
Rehabilitation
Reproducibility of Results
Pattern recognition
Ranging
Hand
Linear discriminant analysis
020601 biomedical engineering
Support vector machine
Muscle Fatigue
Outlier
Female
020201 artificial intelligence & image processing
Artificial intelligence
Artifacts
business
Classifier (UML)
Algorithms
Subjects
Details
- ISSN :
- 15580210 and 15344320
- Volume :
- 27
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Accession number :
- edsair.doi.dedup.....dac203ef6804fefa130ff3545fbd4454