The construction industry is characterized by physically demanding tasks and the adoption of awkward postures, both of which contribute to a high incidence of work-related musculoskeletal disorders (WMSDs). Despite the significance of these factors, few studies considered the external load estimation that considers the actual weights being lifted and carried in ergonomic assessments. This research aims to enhance the accuracy of WMSD risk evaluations by integrating external load estimation into ergonomic assessments. We utilized skeleton tracking technology to automatically evaluate awkward postures based on the Rapid Upper Limb Assessment (RULA) framework, a method for evaluating the exposure of workers to ergonomic risk factors. Concurrently, we analyzed electromyography (EMG) signals measuring muscle activity to extract pertinent features for estimating external loads, which were subsequently integrated into the overall ergonomic assessment. Experimental results demonstrate that the Multi-Layer Perceptron-Back Propagation algorithm outperforms alternative machine learning classification methods, achieving an accuracy rate of 98.3%.
Tian et al. (Thu,) studied this question.