EMG pattern recognition control of multifunctional prostheses by transradial amputees

Electromyogram (EMG) pattern recognition approach has been investigated widely with able-bodied subjects for control of multifunctional prostheses and verified with high performance in identifying different movements. However, it remains unclear whether transradial amputees can achieve similar performance. In this study, we investigated the performance of EMG pattern recognition control of multifunctional transradial prostheses in five subjects with unilateral below-elbow amputation. Testing results on both residual and intact arms showed that the average classification error (21%) of amputated arms for ten motion classes (four wrist movements, six hand grasps) and a ‘no movement’ class over all five subjects was about 15% higher than that of intact arms. For six basic motion classes (wrist flexion/extension, wrist pronation/supination, and hand open/close), the average classification error over all five subjects was about 7% from residual arms, which was similar to the result from intact arms (6%). Only six optimal electrode channels might be needed to provide an excellent myoelectric control system for the six basic movements. These results suggest that the muscles in the residual forearm may produce sufficient myoelectric information to allow the six basic motion control, but insufficient information for more hand functions with fine finger movements.

[1]  R N Scott Myoelectric control of prostheses. , 1966, Archives of physical medicine and rehabilitation.

[2]  George N. Saridis,et al.  EMG Pattern Analysis and Classification for a Prosthetic Arm , 1982, IEEE Transactions on Biomedical Engineering.

[3]  R.N. Scott,et al.  A new strategy for multifunction myoelectric control , 1993, IEEE Transactions on Biomedical Engineering.

[4]  S H Park,et al.  EMG pattern recognition based on artificial intelligence techniques. , 1998, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[5]  K. Englehart,et al.  Classification of the myoelectric signal using time-frequency based representations. , 1999, Medical engineering & physics.

[6]  S Micera,et al.  A hybrid approach to EMG pattern analysis for classification of arm movements using statistical and fuzzy techniques. , 1999, Medical engineering & physics.

[7]  F. K. Lam,et al.  Fuzzy EMG classification for prosthesis control. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[8]  Kevin B. Englehart,et al.  A robust, real-time control scheme for multifunction myoelectric control , 2003, IEEE Transactions on Biomedical Engineering.

[9]  Adrian D. C. Chan,et al.  Continuous myoelectric control for powered prostheses using hidden Markov models , 2005, IEEE Transactions on Biomedical Engineering.

[10]  R.Fff. Weir,et al.  A heuristic fuzzy logic approach to EMG pattern recognition for multifunctional prosthesis control , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[11]  L. E. Peppard,et al.  Feature-based classification of myoelectric signals using artificial neural networks , 1998, Medical and Biological Engineering and Computing.

[12]  Junuk Chu,et al.  A Real-Time EMG Pattern Recognition System Based on Linear-Nonlinear Feature Projection for a Multifunction Myoelectric Hand , 2006, IEEE Transactions on Biomedical Engineering.

[13]  N.V. Thakor,et al.  Towards the Control of Individual Fingers of a Prosthetic Hand Using Surface EMG Signals , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[14]  M. Swiontkowski Targeted Muscle Reinnervation for Real-time Myoelectric Control of Multifunction Artificial Arms , 2010 .