Paper Info
Title
Motion Recognition For Simultaneous Control Of Multifunctional Transradial Prostheses
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.
Year
DOI
Venue
2013
10.1109/EMBC.2013.6609822
2013 35TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Keywords
Field
DocType
classification algorithms,accuracy,biomechanics,electrodes,pattern recognition
Prosthesis,Computer vision,Pattern recognition,Motion recognition,Computer science,Electromyography,Speech recognition,Signal classification,Artificial intelligence,Biomechanics,Classifier (linguistics)
Conference
Volume
Issue
ISSN
2013
null
1557-170X
Citations 
PageRank 
References 
0
0.34
4
Authors
5
Name
Order
Citations
PageRank
Naifu Jiang110.71
Lan Tian264.38
Peng Fang33015.63
Yaping Dai4239.51
Guanglin Li531457.23