This paper presents an embedded system that detects inreal time the movement intention to control a prosthetichand. This work shows that using temporal characteristicsof simple calculation; it is possible to obtain subsets offeature vectors discernible enough as to use simple patternclassifiers. Thus, in this paper a classifier is proposed whichis based on the minimum distance from the centroid ofthe groups characterizing the movements to identify,modifying the known algorithm K-nearest neighbors.Movement intention classification results obtained fromthe developed system are shown; using the percentageof success as an effectiveness measurement, by conductingtests over three persons with healthy muscles. Theexperimental results show that this system can be usedeffectively for the control of execution of four motorprimitives on a prosthetic robotic hand.
Electromyography (EMG), pattern recognition, K-nearest neighbors, prosthetic robotic handElectromiografía (EMG), reconocimiento de intención de movimiento, prótesis de mano robóticas, K-vecinos más próximos
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