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Active back-support exoskeleton has gained recognition as a potential solution to mitigate work- related musculoskeletal disorders. However, their utilization in the construction industry can introduce unintended consequences, such as increased fall hazards. This study examines the implications of using active back-support exoskeleton on fall risk during construction framing tasks, incorporating wearable pressure insoles for data collection. Two experimental conditions were established, one involving the simulation of construction framing tasks with exoskeleton and the other without exoskeleton. These tasks encompassed six subtasks: measuring, assembly, nailing, lifting, moving, and installation. Foot plantar pressure distribution was recorded across various spatial foot regions, including the arch, toe, metatarsal, and heel. Statistical analysis, employing a paired t-test on peak plantar pressure data, revealed that the use of active back-support exoskeleton significantly increased fall risks in at least one of the foot regions for all subtasks, except for the assembly subtask. These findings provide valuable insights for construction stakeholders when making decisions regarding the adoption of active back-support exoskeleton in the industry. Moreover, they inform exoskeleton manufacturers of the need to develop adaptive and customized exoskeleton solutions tailored to the unique demands of construction sites.

 

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. 

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. 

Powered exoskeletons have the potential to reduce the physical demands on construction workers and enhance their abilities, yet adoption of this technology has been limited in the US construction sector. To that end, this study aimed to identify the barriers to the adoption of powered exoskeletons in the US construction industry. Firstly, a literature review was conducted to identify commercially available powered exoskeletons suitable for construction. Concurrently,questionnaires were developed and distributed among construction practitioners to understand the challenges associated with the implementation of powered exoskeletons in the US construction industry. The results showed that concerns about usability and productivity gains were the main barriers to the adoption of exoskeletons in the construction sector. The findings of this study provide valuable insights for improving the adoption and implementation of powered exoskeletons in the US construction industry, which could enhance worker safety and productivity. 

Active exoskeletons are emerging as ergonomic solutions in the construction sector to reduce work-related musculoskeletal injuries. While the benefits of active exoskeletons are promising, they can also cause increased muscle activity, leading to local muscular fatigue. This study aimed to examine the impact of the active exoskeleton system on the muscular activity of construction workers during common construction activities. Ten subjects completed material handling tasks under two weight conditions (10 and 30 lbs) in a lab-controlled environment, with and without using an active exoskeleton. Portable electromyography (EMG) sensors were used to measure lumbar erector spinae (LES) muscle activity in each condition. Four descriptive statistics features in the time and frequency domains were extracted from the collected signals. Results of the t-test showed a significant difference in the physiological metrics extracted from the subjects’ EMG signals of the LES muscle. Findings demonstrated that using active exoskeletons reduces the internal muscle force in the lower back regions of construction workers.