Physical Load Analysis of Dynamic Movement of Construction Workers Using A Biomechanical Model

Abstract

Construction workers are highly susceptible to work-related musculoskeletal disorders (WMSD). The construction sector faces multiple risk factors, including excessive physical demands, repetitive motions, and heavy lifting, which strain muscles and joints and can lead to injuries, necessiating accurate fatigue monitoring. Traditionally, fatigue monitoring in construction relies on self-reports and questionnaires, which are inefficient for continuous assessment. Some researchers have used body sensors, but these can be invasive and uncomfortable, leading to skin irritation.

To improve fatigue monitoring, this study proposes a non-invasive approach designed for construction workers. This method allows for a comprehensive assessment of whole-body fatigue. The research involves developing a 3D motion capture algorithm to model body movements, while a force plate tracks ground reaction forces during various work modes. Integrating data from the force plate, motion capture, and musculoskeletal modeling enables the creation of fatigue evaluation metrics.

Dynamic biomechanical simulations using OpenSim software analyzed the stress experienced by workers during heavy lifting. Results showed that squatting generates less torque on the lower back compared to bending but requires greater strength from gluteal and leg muscles. The study also examined muscle activation patterns and joint torques for different lifting techniques. Bending produced higher torque on the lower back, increasing injury risk, while squatting allowed for a more balanced torque distribution.

Overall, construction workers face significant biomechanical stress, particularly in the lower back and knees. Limitations of the study include equipment complexity and participants' lack of bricklaying experience. Future research should involve more experienced workers to enhance applicability and explore training effects on neuromuscular adaptation, ultimately aiming to improve safety and well-being in the construction industry.

Date of Award	2025
Original language	English
Awarding Institution	The Hong Kong University of Science and Technology
Supervisor	Yantao YU (Supervisor)