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Shared and specific synchronous muscle synergies arisen from optimal feedback control theory
, Article 2009 4th International IEEE/EMBS Conference on Neural Engineering, NER '09, Antalya, 29 April 2009 through 2 May 2009 ; 2009 , Pages 155-158 ; 9781424420735 (ISBN) ; Vahdat, S ; Vosoughi Vahdat, B ; National Institutes of Health, NIH; National Institute of Neurological Disorders and Stroke, NINDS; National Science Foundation, NSF ; Sharif University of Technology
2009
Abstract
In this study the properties of muscle synergies, arising from optimal feedback control are investigated. Three different tasks namely reaching, via-point, and hitting are performed using optimization of corresponding cost functions. Then by applying non-negative matrix factorization method to a dataset of muscle tensions, synchronous muscle synergies are obtained. In this way, different muscle patterns can be generated by linear combination of these basis vectors with non-negative time-varying scaling coefficients. According to our simulations some of obtained muscle synergies are shared between tasks and some of them are specific for one task. This finding is also in agreement with the...
Neuro-fuzzy surface EMG pattern recognition for multifunctional hand prosthesis control
, Article 2007 IEEE International Symposium on Industrial Electronics, ISIE 2007, Caixanova - Vigo, 4 June 2007 through 7 June 2007 ; 2007 , Pages 269-274 ; 1424407559 (ISBN); 9781424407552 (ISBN) ; Jahed, M ; Sadati, N ; Sharif University of Technology
2007
Abstract
Electromyogram (EMG) signal is an electrical manifestation of muscle contractions. EMG signal collected from surface of the skin, a non-invasive bioelectric signal, can be used in different rehabilitation applications and artificial extremities control. This study has proposed to utilize the surface EMG (SEMG) signal to recognize patterns of hand prosthesis movements. It suggests using an adaptive neuro-fuzzy inference system (ANFIS) to identify motion commands for the control of a prosthetic hand. In this work a hybrid method for training fuzzy system, consisting of back-propagation (BP) and least mean square (LMS) is utilized. Also in order to optimize the number of fuzzy rules, a...