Comparing Evolvable Hardware to Conventional Classifiers for Electromyographic Prosthetic Hand Control

Kyrre Glette, Thiemo Gruber, Paul Kaufmann, Jim Torresen, Bernhard Sick, Marco Platzner
2008 2008 NASA/ESA Conference on Adaptive Hardware and Systems  
Evolvable hardware has shown to be a promising approach for prosthetic hand controllers as it features selfadaptation, fast training, and a compact system-on-chip implementation. Besides these intriguing features, the classification performance is paramount to success for any classifier. However, evolvable hardware classifiers have not yet been sufficiently compared to state-of-the-art conventional classifiers. In this paper, we compare two evolvable hardware approaches for signal
more » ... to three conventional classification techniques: knearest-neighbor, decision trees, and support vector machines. We provide all classifiers with features extracted from electromyographic signals taken from forearm muscle contractions, and try to recognize eight different hand movements. Experimental results demonstrate that evolvable hardware approaches are indeed able to compete with state-of-the-art classifiers. Specifically, one of our evolvable hardware approaches delivers a generalization performance similar to that of support vector machines.
doi:10.1109/ahs.2008.12 dblp:conf/ahs/GletteTGSKP08 fatcat:carky2if2zcobnp5237vxdms4q