Developing an optimization model for minimizing musculoskeletal stress in repetitive motion tasks
Abstract
Repetitive motion tasks are widely prevalent in various industries, including manufacturing and office environments, often leading to significant musculoskeletal stress and associated injuries. The continuous nature of these tasks, coupled with improper posture, excessive force exertion, and inadequate rest periods, exacerbates the risk of long-term damage to muscles, joints, and tendons. This paper presents a novel approach to minimizing musculoskeletal stress by developing a Reinforcement Learning (RL)—based optimization model. The model dynamically adjusts real-time task parameters, such as posture, speed, and force exertion, to reduce joint load, muscle activation, and cumulative fatigue while maintaining task performance and productivity. Data was collected from 45 participants performing repetitive tasks in a controlled laboratory environment. Key biomechanical factors, including joint load, muscle activation, and cumulative fatigue, were measured using motion capture, electromyography (EMG), and force plate systems. The RL was trained and validated using this data, with significant improvements observed across all key metrics. The results demonstrated that the model achieved an average reduction of 25%–28% in joint load, 23%–29% in muscle activation, and 26%–28% in cumulative fatigue. In addition, task completion times and accuracy were maintained or improved, demonstrating the model’s effectiveness in balancing ergonomic benefits with productivity. This study provides an integrated approach to reducing musculoskeletal stress while ensuring task efficiency, offering a dynamic, data-driven solution that can be applied across various industries. The findings suggest that RL optimization can significantly improve worker health and task sustainability without compromising organizational performance.
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