Investigating machine learning approaches for hand pose estimation and ergonomic risk assessment

Musculoskeletal disorders pose a significant occupational health challenge, impairing worker well-being and operational efficiency, particularly in manufacturing. The risks of improper postures require effective identification and mitigation strategies. Current assessment methods rely on subjective, qualitative measures and lack robust numerical metrics, especially in hand pose estimation. This study introduces an advanced machine learning algorithm designed for the identification and classification of hand poses using inertial gloves. By tracking hand positions, the proposed approach facilitates objective risk assessments during work activities. Experimental findings validate the system's efficacy, indicating its potential to improve ergonomic analysis and enhance workplace safety.