Hybrid EMG classifier based on HMM and SVM for hand gesture recognition in prosthetics

Pattern recognition and classification algorithms are widely studied in natural gesture interfaces for upper limb prostheses. Robustness and accuracy of control systems are key challenge in such applications. To improve the classification performance, the conventional approach builds on classifier parameters tuning and/or feature extraction techniques. In this paper, we propose a complementary approach based on the combination of two heterogeneous classifiers: the Support Vector Machines and the Hidden Markov Models. This technique takes advantage of the robust time-independent classification of the SVM taking into account the information about history of the signal with the HMM. We demonstrate that, independently from the performance of the SVM, which can be further optimized with typical methods, the combined approach gains 12% recognition accuracy. We further comment on the applicability of this approach in resource constrained embedded implementations considering real-time requirements in the field of prosthesis control.