Automatic video-based monitoring of physically demanding tasks for biomechanical risk assessment

Musculoskeletal disorders (MSDs) represent a major concern in occupational health, impacting millions of workers globally. These conditions arise from repetitive movements, awkward postures, and excessive physical strain. As manual handling tasks continue to be widespread across various industries, a deeper understanding of the risk factors contributing to MSDs has become increasingly important. The use of systems for direct measurements of human movements that do not rely on wearable devices enables operators to carry out tasks in their regular work attire, making them convenient for real-world applications. This study explores the implementation of low-cost, video-based monitoring systems for the assessment of manual handling tasks, with the objective of detecting biomechanically suboptimal postures associated with an elevated risk of musculoskeletal disorders (MSDs). The validity of the measurements obtained using a standard RGB camera was verified through comparison with reference data provided by the Vicon gold standard system. The automatic analysis of postures — whether correct or incorrect — observed in a real experimental dataset was performed using unsupervised clustering techniques, following validation by domain experts. The models derived from this analysis were subsequently employed to classify new task executions and automatically detect postural anomalies. The proposed system demonstrated sufficient accuracy for the automatic detection of risk-related behaviors, highlighting its potential to enhance workplace safety.