Modeling and visualization of classification-based control schemes for upper limb prostheses

Abstract

Duringthedevelopmentof controlschemes for upper-limbpros-theses, the selection of a classification method is the decisive factor on predicting the correct hand movements. This contribution brings forward an approach to validate and visualize the output of a chosen classifier by simulative means. Using features extracted from a collection of recorded myoelectric signals (MES), a training set for different classes of hand movements is produced and validated with additional MES recordings. Using the output of the classifier, the behavior of an actual prosthesis is simulated by controlling the 3D model of a prosthetic hand. For system-atic comparison of feature sets and classification methods, a toolbox for MATLABTM has been developed. Our classification results show, that ex-isting classification schemes based on EMG data can be improved signif-icantly by adding NIR sensor data. Employing only two combined EMG-NIR sensors, five motion classes comprising full movements, including pronation and supination, can be distinguished with 100% accuracy.