Quantitative Evaluation of Muscular Risks during Manual Load Handling using Vision-based System and Biomechanical Modeling*

Abstract

Musculoskeletal disorders (MSDs) continue to represent one of the primary causes of pain, disability, and productivity loss, especially in work environments characterized by repetitive activities and manual material handling. This paper presents a vision-based system to investigate the muscular activities and risks associated with workers performing tasks such as lifting, carrying, and placing a load from a ground pallet to a shelf. A stereocamera setting, utilizing two commercial devices, is used to collect paired images of the actions performed by users. A deep learning model is employed to detect 2D skeletal keypoints, which are then projected into the 3D space using calibration data. Then, the obtained 3D keypoints of the worker’s skeleton enable to derive the biomechanical model of the user by using the OpenSim framework. In this way, it is possible to import the movements of subjects and to perform the force and joint reaction analyses. Different performing metrics are introduced to quantify the duration of muscular activity, the biomechanical efficiency, and the physiological risk associated with different lifting modalities. The results confirm that the proposed approach can provide a first indication of workplace operational configurations that minimize physiological effort and muscular risk for workers.