Machine learning-enhanced back muscle strength prediction considering lifting condition and individual characteristics.

This study investigated factors influencing back muscle strength, focusing on sex, forearm posture and lifting height. Lower back pain, prevalent in industries involving manual materials handling, is closely linked to back muscle strength. The study analyzed data from 98 participants using machine learning models such as linear regression, random forest and multilayer perceptron (MLP). Results showed significant effects of sex, forearm posture and lifting height on back strength. Males demonstrated higher strength than females, and a pronated forearm posture increased strength by 10% compared to supination. The MLP model achieved the highest predictive accuracy (r = 0.896), outperforming other models. These findings offer valuable insights for designing ergonomic workstations and personalized rehabilitation programs, reducing the risk of work-related musculoskeletal disorders. By addressing critical factors, this study contributes to optimizing occupational safety and healthcare strategies.