Studying the Effects of Back-Support Exoskeletons on Workers’ Cognitive Load during Material Handling Tasks

Abstract

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 in reducing the risk of WMSDs, there is a lack of understanding about the potential effects of exoskeletons on workers’ psychological states. This lack of knowledge raises concerns that exoskeletons may lead to psychological risks, such as cognitive overload, among workers. To bridge this gap, this study aims to assess the impact of back-support exoskeletons (BSE) on workers’ cognitive load during material lifting tasks. To accomplish this, a physiologically based cognitive load assessment framework was developed. This framework used wearable biosensors to capture the physiological signals of workers and applied Autoencoder and Ensemble Learning techniques to train a machine learning classifier based on the signals to estimate cognitive load levels of workers while wearing the exoskeleton. Results showed that using BSE increased workers’ cognitive load by 33% compared to not using it during material handling tasks. The findings can aid in the design and implementation of exoskeletons in the construction industry.