Abstract
Manual material handling tasks, such as lifting and lowering, are ubiquitous across industry sectors. Overexertion during these tasks is among the leading causes of workplace injuries. Previous studies have shown that lifting load is a key factor in determining the risk of injury. However, existing methods for measuring the lifting load often rely on manual measurements, sensor fusion, or other techniques that are difficult to scale in practice. In this study, we present a vision-based approach to automatically predict lifting load by analyzing human body pose trajectories extracted from video alone. Specifically, our method employs person detection, visual tracking, and human body pose estimation to extract pose trajectories and their kinematic features, which are then used to train a Transformer model for load prediction. To evaluate our method, we conducted a human subjects study of 19 participants performing various lifting and lowering tasks with varying postures. Our method achieved an average accuracy of 74.8% to distinguish between light vs. heavy objects, and an average accuracy of 50.8% to identify three levels of lifting loads (light, medium, heavy) across lifting and lowering tasks. These results demonstrate a first step towards computer vision based solutions for automatic, noninvasive, scalable injury risk assessment for manual material handling tasks.