NDLI: Motion recognition for simultaneous control of multifunctional transradial prostheses

Content Provider	IEEE Xplore Digital Library
Author	Naifu Jiang Lan Tian Peng Fang Yaping Dai Guanglin Li
Copyright Year	2013
Description	Author affiliation: Key Lab. of Health Inf. of Chinese Acad. of Sci. (CAS), Shenzhen Inst. of Adv. Technol., Shenzhen, China (Naifu Jiang; Lan Tian; Peng Fang; Guanglin Li) \|\| Sch. of Autom., Beijing Inst. of Technol., Beijing, China (Yaping Dai)
Abstract	Electromyography (EMG) pattern-recognition based control strategies for multifunctional myoelectric prosthesis systems have been studied commonly in a controlled laboratory setting. Most previous efforts concentrated on evaluating the performance of EMG pattern-recognition algorithms in identifying one signal movement at a time. Therefore, the current motion classification methods would be limited with the difficulties in identifying the combined upper-limb motion classes that are commonly required in performing activities daily. In this paper, four improved classifier training schemes were proposed and investigated to address the difficulties mentioned above. Our preliminary results showed that three of the four proposed training schemes could improve the classification performance. The average classification accuracies of the three methods were 75.10% ± 9.71%, 76.95% ± 8.02%, and 77.56% ± 6.55% for the able-bodied subjects, and 63.38% ± 7.51%, 62.55% ± 9.06%, and 62.50% ± 9.36% for the transradial amputees, respectively. These results suggested that the proposed methods could provide better classification performance in identifying the combined motions than the current methods.
Starting Page	1603
Ending Page	1606
File Size	784728
Page Count	4
File Format	PDF
ISBN	9781457702167
ISSN	1557170X
DOI	10.1109/EMBC.2013.6609822
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wrist Accuracy Training Electrodes Electromyography Classification algorithms Pattern recognition
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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