Deep learning-based classification of work-related physical load levels in construction

Abstract

Work-related musculoskeletal disorders (WMSDs) are the leading cause of the nonfatal injuries for construction workers, and a worker’s overexertion is a major source of such WMSDs. Pushing, pulling, and carrying movements—which are all activities largely associated with physical loads—account for 35% of WMSDs. However, most previous studies have focused on the identification of non-ergonomic postures, and there has been limited effort expended on measuring a worker’s exposures to the physical loads caused by materials or tools during construction tasks. With the advantage of using a wearable inertial measurement sensor to monitor a worker’s bodily movements, this study investigates the feasibility of identifying various physical loading conditions by analyzing a worker’s lower body movements. In the experiment with laboratory settings, workers performed a load carrying task by moving concrete bricks. A bidirectional long short-term memory algorithm is employed to classify physical load levels; this approach achieved 74.6 to 98.6% accuracy and 0.59 to 0.99 F-score in classification. The results demonstrate the feasibility of the proposed approach in identifying the states of physical loads. The findings of this study contribute to the literature on classifying ergonomically at-risk workers and on preventing WMSDs in high physical demand occupations, thereby helping enhance the health and safety of the construction workplace.