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Improving the Robustness of Real-Time Myoelectric Pattern Recognition against Arm Position Changes in Transradial Amputees
Joint Authors
Li, Guanglin
Geng, Yanjuan
Samuel, Oluwarotimi Williams
Wei, Yue
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-04-24
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Previous studies have showed that arm position variations would significantly degrade the classification performance of myoelectric pattern-recognition-based prosthetic control, and the cascade classifier (CC) and multiposition classifier (MPC) have been proposed to minimize such degradation in offline scenarios.
However, it remains unknown whether these proposed approaches could also perform well in the clinical use of a multifunctional prosthesis control.
In this study, the online effect of arm position variation on motion identification was evaluated by using a motion-test environment (MTE) developed to mimic the real-time control of myoelectric prostheses.
The performance of different classifier configurations in reducing the impact of arm position variation was investigated using four real-time metrics based on dataset obtained from transradial amputees.
The results of this study showed that, compared to the commonly used motion classification method, the CC and MPC configurations improved the real-time performance across seven classes of movements in five different arm positions (8.7% and 12.7% increments of motion completion rate, resp.).
The results also indicated that high offline classification accuracy might not ensure good real-time performance under variable arm positions, which necessitated the investigation of the real-time control performance to gain proper insight on the clinical implementation of EMG-pattern-recognition-based controllers for limb amputees.
American Psychological Association (APA)
Geng, Yanjuan& Samuel, Oluwarotimi Williams& Wei, Yue& Li, Guanglin. 2017. Improving the Robustness of Real-Time Myoelectric Pattern Recognition against Arm Position Changes in Transradial Amputees. BioMed Research International،Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1137416
Modern Language Association (MLA)
Geng, Yanjuan…[et al.]. Improving the Robustness of Real-Time Myoelectric Pattern Recognition against Arm Position Changes in Transradial Amputees. BioMed Research International No. 2017 (2017), pp.1-10.
https://search.emarefa.net/detail/BIM-1137416
American Medical Association (AMA)
Geng, Yanjuan& Samuel, Oluwarotimi Williams& Wei, Yue& Li, Guanglin. Improving the Robustness of Real-Time Myoelectric Pattern Recognition against Arm Position Changes in Transradial Amputees. BioMed Research International. 2017. Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1137416
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1137416