A Study to Automate Workload Assessment Procedure using Computer Vision

In the field of occupational health, accurate and efficient ergonomic risk assessment is of paramount importance. This study presents an innovative computer vision-based approach to automate workload assessment with the goal of preventing work-related musculoskeletal disorders (WMSDs). Our methodology, which uses a two-step algorithm, captures 3D skeletal representations from video footage of workers performing tasks such as lifting, lowering, and carrying. We conducted experiments stratified by object weight and task height to simulate different workplace scenarios. Our system uses AlphaPose for human pose estimation and MotionBERT for 3D skeleton extraction to provide a dynamic and accurate assessment of workload. By analyzing spatial and contextual data, this method provides real-time ergonomic risk assessment, which is critical in today’s variable work environments.