DocumentCode :
139001
Title :
Boosting training for myoelectric pattern recognition using Mixed-LDA
Author :
Jianwei Liu ; Xinjun Sheng ; Dingguo Zhang ; Xiangyang Zhu
Author_Institution :
State Key Lab. of Mech. Syst. & Vibration, Shanghai Jiao Tong Univ., Shanghai, China
fYear :
2014
fDate :
26-30 Aug. 2014
Firstpage :
14
Lastpage :
17
Abstract :
Pattern recognition based myoelectric prostheses (MP) need a training procedure for calibrating the classifier. Due to the non-stationarity inhered in surface electromyography (sEMG) signals, the system should be retrained day by day in long-term use of MP. To boost the training procedure in later periods, we propose a method, namely Mixed-LDA, which computes the parameters of LDA through combining the model estimated on the incoming training samples of the current day with the prior models available from earlier days. An experiment ranged for 10 days on 5 subjects was carried out to simulate the long-term use of MP. Results show that the Mixed-LDA is significantly better than the baseline method (LDA) when few samples are used as training set in the new (current) day. For instance, in the task including 13 hand and wrist motions, the average classification rate of the Mixed-LDA is 88.74% when the number of training samples is 104 (LDA: 79.32%). This implies that the approach has the potential to improve the usability of MP based on pattern recognition by reducing the training time.
Keywords :
electromyography; medical signal processing; pattern recognition; prosthetics; Mixed-LDA; myoelectric pattern recognition; myoelectric prosthesis; nonstationarity; sEMG signals; surface electromyography; training boost; Accuracy; Computational modeling; Data models; Mathematical model; Pattern recognition; Training; Wrist;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE
Conference_Location :
Chicago, IL
ISSN :
1557-170X
Type :
conf
DOI :
10.1109/EMBC.2014.6943517
Filename :
6943517
Link To Document :
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