Biomechanical assessment of a passive back exoskeleton using vision-based motion capture and virtual modeling
Zhou Yuan, JoonOh Seo, Yue Gong, Kelvin HoLam Heung, Masood Khan, Ting Lei
Abstract
This paper proposes a video-driven biomechanical analysis method for measuring muscular loads influenced by wearing an exoskeleton suit, combining vision-based motion capture and virtual modeling approaches. Motion data obtained from site videos is integrated with a newly developed human-exoskeleton model in biomechanical software, to simulate muscular loads on the human body and evaluate exoskeleton suits. This method has been validated through experimental tests, where simulated and directly measured muscle activations were compared for four types of lifting tasks. The results indicate that this method successfully estimates neuromuscular activations of the low back muscles with and without wearing an exoskeleton suit, though the effect of the exoskeleton suit tends to be overestimated in simulations. Despite this limitation, the proposed method is expected to assist in efficiently evaluating exoskeleton use in practice, thereby facilitating the more widespread adoption of passive exoskeletons in construction. • A video-driven simulation approach is proposed for evaluating passive exoskeletons. • Vision-based motion capture data is used to simulate construction tasks. • Simulated biomechanical loads accurately reflect actual muscle loads. • The method contributes to building a virtual exoskeleton development platform.