A Deep-Learning Approach for Task Recognition of Industrial Workers and RULA Score Calculation

Human activity has been closely related to the development of musculoskeletal disorders. Workers in industries such as manufacturing, assembly, and construction often engage in repetitive and various motions, such as lifting heavy objects or performing the same task for a long period. Because such activities can increase the risk of various muscular disorders, it is important to help workers choose the best postures when performing their activities. In this study, we propose a deep learning approach for task recognition and RULA score calculation. Our approach uses a revised version of the Long Term Recurrent Convolutional Neural network-based model to classify work activities based on video input and then applies a separate neural network to estimate the RULA score for each input activity. We trained and evaluated our approach using a dataset of annotated work activities. Our results show that our approach achieves competitive accuracy for activity recognition and RULA score estimation, demonstrating the potential of deep learning for improving ergonomic assessments in the workplace.