Manufacturing Operator Ergonomics: A Conceptual Digital Twin Approach to Detect Biomechanical Fatigue

The primary sources of injuries in the workplace are improper manual material handling (MMH) motions, forklift collisions, slip, and fall. This research presents a Digital Twin (DT) framework to analyze fatigue in humans while performing lifting MMH activity in a laboratory environment for the purpose of reducing these types of injuries. The proposed methodology analyzes the worker’s body joints to detect biomechanical fatigue as a factor of change in back, elbow, and knee joint angles. Using the dynamic time warping (DTW) algorithm, the change in joint angles with time was analyzed. The variation in DTW parameters was evaluated using exponentially weighted moving average (EWMA) control charts for further analysis. A preliminary study considering two healthy male subjects performing seven experiments, each under an optical motion capture system was performed to test the model’s efficiency. Our contributions are twofold. First, we propose a model to detect biomechanical fatigue in the subjects performing MMH lifting activity as a change in joint angles. Secondly, the research also shows evidence that different individuals show signs of body fatigue via different body joints and showcases the need for a true personalized DT for an operator for fatigue assessment in an MMH environment.