MyoPassivity Map: Does Multi-Channel sEMG Correlate With the Energetic Behavior of Upper-Limb Biomechanics During Physical Human-Robot Interaction?

Abstract

The human arm has an intrinsic capacity to absorb energy during physical human-robot interaction (pHRI), which can be identified as biomechanical excess of passivity (EoP). This can be used as a central factor in the development of passivity-based pHRI controllers securing haptic transparency while guaranteeing pHRI stability. Despite its significance, the real-time estimation of EoP remains an under-investigated topic. For the first time, we investigate the relationship between the EoP and muscle activity of the forearm at the wrist joint while analyzing sixteen surface electromyography (sEMG) sensors. The letter explores optimal sensor placement for maximizing the correlation between muscle activity and the estimated EoP. Ten subjects participated in this study. The EoP of the wrist was identified through high-frequency perturbations in four directions, and two instructed co-contraction levels. The results uncover a strong correlation between sEMG and EoP. This paper also reports the effect of the direction of pHRI interaction on the EoP of the wrist, with increased energetic passivity in the abduction-adduction direction compared to supination-pronation. Also, the study investigated the effect of the observation duration for sEMG on the sEMG-EoP correlation (short windows would be required for real-time applications). Although the correlation decreases for shorter windows, it remains relatively high, supporting dynamic estimation of EoP in real-time. Additionally, we found that sEMG sensors near the wrist have the highest correlation with EoP for short windows. The findings of this letter indicate that sEMG encodes significant potential for real-time estimation of EoP in the design of next-generation pHRI controllers supporting concurrent transparency and stability.