Harnessing Artificial Intelligence to Guide Exoskeleton Adoption in Construction

Abstract

The construction industry is a major contributor to the Gross Domestic Product in the United States, yet it continues to experience persistent health and safety challenges from work-related musculoskeletal disorders. These disorders, arising from physically demanding and repetitive tasks, result in muscle fatigue, diminished work capacity, increased lost workdays, long-term disability, and contribute to the shortage of skilled labor. The back is one of the most affected body regions. While efforts have been made to mitigate these disorders through administrative, engineering, and training measures, as well as emerging technologies such as computer vision and wearable sensors, exoskeletons have emerged as a human-centered solution that could extend the longevity and sustainability of the construction workforce. Specifically, active back-support exoskeletons have been identified as a potential solution to the prevalence of back-related musculoskeletal disorders. However, limited evidence exists on the criteria for appropriate selection, adoption and integration of active back-support exoskeleton technology in construction practice. Guided by the Technology-Organization-Environment framework, this research investigates the potential of an Artificial Intelligence-enabled decision support system to assist stakeholders in selecting suitable active back-support exoskeletons for construction tasks. The research followed a multi-stage design. First, facilitators and barriers to adoption were identified using the Delphi technique and semi-structured interviews with construction stakeholders. Next, a laboratory study assessed the physical, physiological, and psychological impacts of active back-support exoskeleton use simulated construction tasks, drawing on biofeedback sensors and subjective evaluations. Building on these insights, a data-driven analytical decision-support system was developed by integrating large language models with digital twin technologies. This system was subsequently evaluated for usability, organizational fit, and environmental compatibility. This research lies at the intersection of construction ergonomics, wearable robotics, and intelligent decision systems. It contributes to an emerging interdisciplinary field by translating biomechanical evidence and Artificial Intelligence-driven analytics into practical decision-support tools aimed at reducing injury risk and enhancing workforce sustainability. Accordingly, this research introduces a human-centered Artificial Intelligence framework for exoskeleton selection in construction, strengthening stakeholders' capacity to make informed adoption decisions.