Ergonomic Risk Prediction for Awkward Postures From 3D Keypoints Using Deep Learning

Abstract

Work-related musculoskeletal ailments are injuries or disorders of the joints, muscles, nerves, or tendons caused by repetitive tasks and jobs that require uncomfortable postures. REBA (Rapid Entire Body Assessment) is a widely used assessment method for examining occupational ergonomics in areas where musculoskeletal disorders (MSDs) are common. REBA assessment necessitates the presence of a professional evaluator who monitors workers’ motions and postures, which takes time and has limitations in terms of real-world implementation. With the progress of deep learning-based human posture estimate algorithms, postural risk assessment has become an important and complex research area. We present a technique for forecasting REBA risk levels using 3D coordinates of human body position as input data in this study. We calculated REBA risk scores for various body segments and overall risk rating for corresponding action level for each body position using 3D keypoints from the widely renowned Human 3.6M dataset, which is a significant contribution for future research work in this arena. Using this vast ground truth dataset, a unique DNN model was created to forecast the REBA risk level for measuring the full body’s postural risk. REBA Ground Truth dataset is highly imbalanced which coped with data augmentation for the rare classes. To determine the optimal model configuration based on highest accuracy, ablation study is conducted by tuning different hyper-parameters. The proposed model, post-ablation study, attained 89.07% accuracy score on a test set of 128,046 samples from Nadam optimizer with a learning rate of 0.001 and batch size of 512.