EMG-control of prostheses by switch signals: extraction and classification of features

This work examines the process of grasp type classification based on electromyographic (EMG-) signals by a recently presented multifunctional control scheme. For the latter the online feature extraction out of EMG-signals is described. Features are used to teach the corresponding signal to the system. The teaching process is based on statistical classifiers, fuzzy rulebases and artificial neural networks, respectively. Since there is no knowledge about which classifier serves best for EMG-data several classifiers are compared using data of seven amputated subjects. Subsequently, a routine is presented which generates source code for a microcontroller implementation.