Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành y học dành cho các bạn tham khảo đề tài: Influence of the training set on the accuracy of surface EMG classification in dynamic contractions for the control of multifunction prostheses | J Mil JOURNAL OF NEUROENGINEERING Hll AND REHABILITATION Influence of the training set on the accuracy of surface EMG classification in dynamic contractions for the control of multifunction prostheses Lorrain et al. Lorrain et al. Journal of NeuroEngineering and Rehabilitation 2011 8 25 http content 8 1 25 9 May 2011 BioMed Central Lorrain et al. Journal of NeuroEngineering and Rehabilitation 2011 8 25 http content 8 1 25 Iril JOURNAL OF NEUROENGINEERING NCR AND REHABILITATION RESEARCH Open Access Influence of the training set on the accuracy of surface EMG classification in dynamic contractions for the control of multifunction prostheses Thomas Lorrain1 Ning Jiang2 3 and Dario Farina2 Abstract Background For high usability myo-controlled devices require robust classification schemes during dynamic contractions. Therefore this study investigates the impact of the training data set in the performance of several pattern recognition algorithms during dynamic contractions. Methods A 9 class experiment was designed involving both static and dynamic situations. The performance of various feature extraction methods and classifiers was evaluated in terms of classification accuracy. Results It is shown that combined with a threshold to detect the onset of the contraction current pattern recognition algorithms used on static conditions provide relatively high classification accuracy also on dynamic situations. Moreover the performance of the pattern recognition algorithms tested significantly improved by optimizing the choice of the training set. Finally the results also showed that rather simple approaches for classification of time domain features provide results comparable to more complex classification methods of wavelet features. Conclusions Non-stationary surface EMG signals recorded during dynamic contractions can be accurately classified for the control of multi-function prostheses. Background The myoelectric signals can
