Physical ergonomics anticipation with human motion prediction

Abstract

Good physical ergonomics is a crucial aspect of performing repetitive tasks sustainably for a long period. We developed a VR training environment that improves the ergonomics and experience of the user during a task. Through human motion prediction, we can predict the posture of the user accurately up to three seconds ahead of time. Based on this posture, a physical ergonomics score, called REBA (Hignett and McAtamney, 2000), is computed and can warn the user ahead of time to adapt their posture. We used the lightweight STS-GCN model (Sofianos et al., 2021) as it can infer predictions in real-time to give feedback to the users. We show in our experiments that using multi-task learning improves human motion prediction significantly. Our method is generally applicable for various manual tasks as almost all tasks can be simulated in a VR environment.