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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. 

Traditional lectures have difficulties instilling pragmatic skills in construction engineering students due to the inability to illuminate the complexities within the human-robot collaborative construction environment. While on-site can acclimatize construction students to reality and construct knowledge that can solve safety challenges, it is challenging to organize on-site training trips owing to the dangerous nature of construction workplaces. This research aimed to explore virtual reality (VR) as a tool to enhance students’ perception and knowledge of construction robotic safety. For this purpose, the study developed a virtual training platform for providing construction engineering students with safety knowledge on interacting with simulated robots within the virtual environment of construction sites. A self-assessment approach was leveraged among 20 recruited students to demonstrate the efficacy of students’ engagement and learning outcomes from the proposed learning approach over the traditional learning approach. Results indicated a statistical difference in students’ learning outcomes and engagement levels between the developed approach and the traditional approach. Findings demonstrated the implications of VR as an experiential tool to enhance the students’ learning of robotic safety in construction.