CLINICALLY PRACTICAL APPLICATIONS OF PATTERN RECOGNITION FOR MYOELECTRIC PROSTHESES

The promise of pattern recognition for improved control of upper-extremity powered prostheses has existed for a long time. During the years of offline research and algorithm development, very little experience has been gained with real-time use in clinical and chronic settings. Our group, having the benefit of working with subjects who have undergone targeted muscle reinnervation (TMR) surgery, is at the forefront of real-world application of pattern recognition for upper extremity amputees. Based on our experiences, we highlight a progression of myoelectric control schemes from conventional control to enhanced pattern recognition control, stressing the application of simple pattern recognition schemes to replace more conventional control. These clinically practical pattern recognition systems incorporate a realistic number of electrodes and the ability to control available prosthetic components. Our experience suggests how the impending, and initial deployment of pattern recognition-controlled prostheses for daily use can be more approachable than what is depicted in high-dimension studies common in the literature today.

[1]  R.N. Scott,et al.  Two-channel enhancement of a multifunction control system , 1995, IEEE Transactions on Biomedical Engineering.

[2]  Adrian D. C. Chan,et al.  A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses , 2005, IEEE Transactions on Biomedical Engineering.

[3]  D Graupe,et al.  Multifunctional prosthesis and orthosis control via microcomputer identification of temporal pattern differences in single-site myoelectric signals. , 1982, Journal of biomedical engineering.

[4]  Ping Zhou,et al.  Decoding a new neural machine interface for control of artificial limbs. , 2007, Journal of neurophysiology.

[5]  T. Kuiken,et al.  Improved Myoelectric Prosthesis Control Using Targeted Reinnervation Surgery: A Case Series , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[6]  Ping Zhou,et al.  Towards Improved Myoelectric Prosthesis Control: High Density Surface EMG Recording After Targeted Muscle Reinnervation , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[7]  F. Finley,et al.  Myocoder studies of multiple myopotential response. , 1967, Archives of physical medicine and rehabilitation.

[8]  Robert D. Lipschutz,et al.  Targeted reinnervation for enhanced prosthetic arm function in a woman with a proximal amputation: a case study , 2007, The Lancet.

[9]  He Huang,et al.  An Analysis of EMG Electrode Configuration for Targeted Muscle Reinnervation Based Neural Machine Interface , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[10]  R. H. Meier,et al.  Functional Restoration of Adults and Children with Upper Extremity Amputation , 2004 .

[11]  T. Kuiken,et al.  Targeted muscle reinnervation for improved myoelectric prosthesis control , 2005, Conference Proceedings. 2nd International IEEE EMBS Conference on Neural Engineering, 2005..

[12]  L. Lindstrom,et al.  Signal Processing for the Multistate Myoelectric Channel , 1977 .

[13]  Finley Fr,et al.  Myocoder studies of multiple myopotential response. , 1967 .

[14]  Kevin B. Englehart,et al.  A wavelet-based continuous classification scheme for multifunction myoelectric control , 2001, IEEE Transactions on Biomedical Engineering.

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

[16]  Robert D. Lipschutz,et al.  The use of targeted muscle reinnervation for improved myoelectric prosthesis control in a bilateral shoulder disarticulation amputee , 2004, Prosthetics and orthotics international.

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

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

[19]  A. Willsky,et al.  Upper Extremity Limb Function Discrimination Using EMG Signal Analysis , 1983, IEEE Transactions on Biomedical Engineering.

[20]  Mehran Jahed,et al.  Real-time intelligent pattern recognition algorithm for surface EMG signals , 2007, Biomedical engineering online.

[21]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[22]  Alcimar Soares,et al.  The Development of a Virtual Myoelectric Prosthesis Controlled by an EMG Pattern Recognition System Based on Neural Networks , 2004, Journal of Intelligent Information Systems.

[23]  B. Hudgins,et al.  A Real-Time Pattern Recognition Based Myoelectric Control Usability Study Implemented in a Virtual Environment , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

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

[25]  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.

[26]  B Hudgins,et al.  Myoelectric signal processing for control of powered limb prostheses. , 2006, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.