From Wearable Sensor Networks to Markerless Motion Capture for Instrumental-based Biomechanical Risk Assessment in Lifting Activities

Manual material handling is one of the leading causes of work-related low-back disorders, and accurate assessment of biomechanical risk is essential to support prevention strategies. Despite workers’ interest in wearable sensor networks (WSN) for quantifying exposure metrics, these systems still present several limitations, including potential interference with natural movements and workplaces, and concerns about durability and cost-effectiveness. For these reasons, alternative motion capture methods are being explored. Among them, completely markerless (ML) technologies are increasingly applied in ergonomics. This study aims to compare WSN and ML in the evaluation of lifting tasks, focusing on the variables and multipliers used to compute the Recommended Weight Limit (RWL) and the Lifting Index (LI) according to the Revised NIOSH Lifting Equation. We hypothesize that ML systems equipped with multiple cameras may provide reliable and consistent estimation of these kinematic variables, thereby improving risk assessment. Twenty-eight workers performed standardized lifts under three risk conditions. Results showed significant differences between WSN and ML in most measures, except at low risk (LI = 1). Nevertheless, ML consistently showed closer agreement with reference benchmarks and lower variability. These findings highlight the potential of ML approaches to deliver accurate, repeatable, and cost-effective biomechanical risk assessments, particularly in demanding lifting tasks.