Model-Free Reinforcement Learning for Adaptive Hand Activity Ergonomics Risk Control

OCCUPATIONAL APPLICATIONAdaptive decision-making systems can help balance productivity and ergonomic risk in repetitive manual tasks. This study demonstrates how data-driven control of work pace using reinforcement learning can reduce hand-wrist strain while maintaining output. Such systems could support real-time work-rest scheduling in manufacturing and help prevent work-related musculoskeletal disorders.