Benefits of Employing Surface Electromyography Signals for Power-Assisted Control From an Agility Perspective

Abstract

This paper discusses the benefits of employing surface electromyography (sEMG) signals for power-assisted control to recognize human motion intention swiftly and efficiently from an agility perspective. A majority of power-assisted control systems use interaction force and torque (F/T) sensors to recognize human motion intention. However, these sensors have limitations regarding agility as they are fundamentally indirect and delayed measurement sensors. As a direct sensor to recognize human motion intention prior to actual human body movements, this study focuses on elucidating the benefits of sEMG-based power-assisted control, which can serve as a complementary and synergetic method alongside the widely used F/T-based power-assisted control. Our experimental (n =11) results suggest that sEMG-based power-assisted control can increase the agility of inherent body movement through correct and rapid recognition of intention. We evaluated agility with respect to muscle usage and elapsed time for task completion, considering the meaning of agility. The results are as follows: the sEMG-based method reduced 1) muscle usage by 29.45% for the trajectory following task, 2) muscle usage by 25.92% and elapsed time by 5.61% for the step response task, and 3) muscle usage by 16.68% and elapsed time by 7.14% for the maximum speed of repetitive movement task, all compared to those of inherent body movement.

Keywords

• Agility
• exoskeleton
• sEMG
• motion intention recognition
• physical human-robot interaction
• power-assisted control