AI-Powered Computer Vision for Ergonomic Risk Assessment and Musculoskeletal Symptom Prevalence in Industrial Metal Polishing Operators

Abstract

Manufacturing polishing tasks involve repetitive movements and sustained postures that increase exposure to work-related musculoskeletal disorders (WRMSDs). This study presents an intersectoral validation of the ergonomic assessment methodology applied to industrial metal polishing operators that considered sociodemographic, anthropometric, and health variables. This study surveyed 41 workers using the Nordic Musculoskeletal Questionnaire and assessed a subsample of 27 workers using the REBA method through AI-based computer vision. Symptom prevalence was highest in the neck (82.9%), shoulders (70.8%), lower back (68.3%), and wrists/hands (65.9%). Using a computer-vision AI-based tool to analyse posture, the REBA method identified moderate (70.3%), high (26.0%) and very high (3.7%) WRMSD risks for the upper arms, neck, and trunk, respectively, with women showing greater susceptibility. Spearman correlation analysis revealed significant associations between age, gender, health perception, and musculoskeletal risks. The findings confirm the ergonomic assessment method’s applicability and reliability for ergonomic risk assessment in industrial polishing tasks, emphasising the need for targeted interventions adapted to gender and age profiles to mitigate occupational hazards. The results support a non-intrusive assessment approach suitable for industrial deployment and for prioritising targeted, worker-stratified ergonomic interventions.