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Work-related musculoskeletal disorders (WMSDs) are a leading cause of injury for workers who are performing physically demanding and repetitive construction tasks. With recent advances in robotics, wearable robots are introduced into the construction industry to mitigate the risk of WMSDs by correcting the workers’ postures and reducing the load exerted on their body joints. While wearable robots promise to reduce the muscular and physical demands on workers to perform tasks, there is a lack of understanding of the impact of wearable robots on worker ergonomics. This lack of understanding may lead to new ergonomic injuries for worker swearing exoskeletons. To bridge this gap, this study aims to assess the workers’ ergonomic risk when using a wearable robot (back-support exoskeleton) in one of the most common construction tasks, material handling. In this research, a vision-based pose estimation algorithm was developed to estimate the pose of the worker while wearing a back-support exoskeleton. As per the estimated pose, joint angles between connected body parts were calculated. Then, the worker’s ergonomic risk was assessed from the calculated angles based on the Rapid Entire Body Assessment (REBA) method. Results showed that using the back-support exoskeleton reduced workers’ ergonomic risk by 31.7% by correcting awkward postures of the trunk and knee during material handling tasks, compared to not using the back-support exoskeleton. The results are expected to facilitate the implementation of wearable robots in the construction industry. 

Exoskeletons, also known as wearable robots, are being studied as a potential solution to reduce the risk of work-related musculoskeletal disorders (WMSDs) in construction. The exoskeletons can help enhance workers’ postures and provide lift support, reducing the muscular demands on workers while executing construction tasks. Despite the potential of exoskeletons inreducing the risk of WMSDs, there is a lack of understanding about the potential effects ofexoskeletons on workers’ psychological states. This lack of knowledge raises concerns thatexoskeletons may lead to psychological risks, such as cognitive overload, among workers. Tobridge this gap, this study aims to assess the impact of back-support exoskeletons (BSE) onworkers’ cognitive load during material lifting tasks. To accomplish this, a physiologically basedcognitive load assessment framework was developed. This framework used wearable biosensorsto capture the physiological signals of workers and applied Autoencoder and Ensemble Learningtechniques to train a machine learning classifier based on the signals to estimate cognitive loadlevels of workers while wearing the exoskeleton. Results showed that using BSE increasedworkers’ cognitive load by 33% compared to not using it during material handling tasks. Thefindings can aid in the design and implementation of exoskeletons in the construction industry.