EMG-force-sensorless power assist system control based on Multi-Class Support Vector Machine

This paper aims to describe a framework implementing Multi-Class Support Vector Machine (MCSVM)-based motion intention recognition. To this end, we primarily constructed a wearable exoskeleton robot of lower body (TTI-Exo) which is employed as the experimentation platform to test the proposed method of motion intention recognition based on MCSVM and the assist effectiveness as well. Experiments of stand-to-sit and sit-to-stand movements were carried out to test the MCSVM method and TTI-Exo's motion assist. Having disclosed prototype development, experimental results are presented. We verified that our proposed method based on MCSVM obtained a better recognition accuracy than a conventional method based on threshold values. Muscle activities when subjects wearing TTI-Exo were much smaller than when subjects not wearing the exoskeleton, thus implying the assist efficacy of our power assist system.