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Model-Free Reinforcement Learning for Adaptive Hand Activity Ergonomics Risk Control
Haozhi Chen1, Peiran Liu1, Haochen Feng2
1Edwardson School of Industrial Engineering, Purdue University, West Lafayette, IN, USA.
Summary
Adaptive systems using reinforcement learning can manage work pace to reduce hand-wrist strain in repetitive tasks. This approach maintains productivity while preventing work-related musculoskeletal disorders.
Area of Science:
- Occupational health and safety
- Ergonomics
- Artificial intelligence in manufacturing
Background:
- Repetitive manual tasks pose risks for musculoskeletal disorders.
- Balancing productivity and worker well-being is a key challenge in manufacturing.
- Current work-rest scheduling methods may not be optimized for individual ergonomic risk.
Purpose of the Study:
- To demonstrate a data-driven approach for adaptive work pace control.
- To reduce hand-wrist strain in repetitive manual tasks using artificial intelligence.
- To maintain or improve productivity while mitigating ergonomic risks.
Main Methods:
- Utilized reinforcement learning for adaptive decision-making.
- Developed a system to control work pace based on real-time ergonomic data.
- Simulated or implemented the system in a manufacturing task context.
Main Results:
- The adaptive system successfully reduced hand-wrist strain.
- Productivity levels were maintained despite pace adjustments.
- Demonstrated the feasibility of real-time work-rest scheduling.
Conclusions:
- Reinforcement learning offers a viable method for optimizing work pace.
- Adaptive systems can enhance worker safety and prevent musculoskeletal disorders.
- This technology has significant potential for manufacturing and other industries.
