Guoyang Zhou (PI), Denny Yu

Purdue University, School of Industrial Engineering

Overexertion in lifting tasks is one of the leading causes of occupational injuries. Lifting loads are the key information practitioners require to evaluate the risks of lifting tasks. However, this is challenging because weight varies across different objects and is unknown in many circumstances. Existing methods of predicting lifting loads focused on analyzing body kinematics or muscles’ electrical activities, which are indirect indicators of weight and require intrusive wearable sensors. This study proposed utilizing tactile gloves embedded with multiple pressure sensors as a new modality to predict lifting loads. Hand pressure data measured by tactile gloves during each lift was formulated as a two-dimensional matrix containing spatial and temporal information. Different types of deep neural networks were adopted, and a transferred ResNet 18 regression model achieved the best performance. Specifically, it achieved a predicted R-squared of 0.821 and a mean absolute error of 1.579 kg. In addition, to understand the model’s decision-making logic and the hand force pattern during lifting, the Shapley Additive Explanations (SHAP) technique was utilized to determine the importance of each sensor at each frame. The results demonstrated that the right hand was more important than the left hand for the model to predict lifting loads. Similarly, fingers were more important than palms, and the middle phase of a lifting task was more important than its beginning and ending phases. Overall, this study demonstrated the feasibility of using tactile gloves to predict lifting loads and provided new scientific insights on hand force exertion during lifting.