Decision-Support and Optimization Framework for Selection of Low-Cost Wearable Devices to Improve the Health and Safety of Mechanical-Trade Workers

Abstract

The construction industry is a major contributor to the Canadian and U.S. economies. However, it continues to face persistent challenges related to worker attrition, productivity loss, and cost inefficiency, driven in large part by the physically demanding nature of the work. Among the injuries that burden this workforce, work-related musculoskeletal disorders (WMSDs) represent one of the largest single categories of nonfatal lost-time injury, consistently occurring at rates higher than the all-industry average. This burden is especially severe in the mechanical trades, including plumbers, pipefitters, welders, refrigeration technicians, and HVAC mechanics, whose work combines overhead installation, kneeling and crouching in confined spaces, manual material handling, and prolonged awkward postures within a single workday. U.S. data show that mechanical trades such as HVAC mechanics and sheet metal workers experience WMSD incidence rates up to 2.5 times the construction average. Canadian workers' compensation data further identify overexertion and repetitive motion as the leading contributors to lost workdays and claim costs in the mechanical trades, with the WMSD-related burden increasing over the past decade. In British Columbia alone, WMSD-related injuries in the mechanical trades accounted for approximately 69,800 lost workdays (39% of the trades’ total) and $39.5 million in claim costs (28%) from 2020 to 2024. WMSD risks vary by task, affected body region, and worker-specific anthropometric, demographic, and health- related characteristics, indicating a clear need for targeted, cost-effective ergonomic interventions matched to each worker and the body regions requiring support.

While wearable support devices, including exoskeletons and exosuits, can mitigate physical loading, full-body systems may provide broader support than a given worker requires, introducing unnecessary costs and adoption barriers related to bulk, motion constraints, and donning burden. To address this gap, this research investigates the physical demands of mechanical-trade work, which is among the most physically taxing in construction, to understand mechanical-trade workers’ exposure to WMSD risk factors and the body regions most affected. Building on this understanding, it develops a multi-criteria decision-support framework that recommends worker-specific combinations of low-cost, lightweight wearable support devices to reduce exposure to WMSD risk factors and enhance worker health, safety, and productivity (HS&P) in the mechanical trades. The objectives of this research are fourfold: (1) to identify industry needs, at-risk body regions, and working postures in mechanical-trade work; (2) to evaluate and classify low-cost wearable support devices; (3) to develop and validate a system that recommends worker-specific device combinations; and (4) to establish the conditions under which the framework applies to other trades. First, it identifies industry needs through a field survey of 13 owner organizations, examines worker injury trends to establish the back, legs, and shoulders as the at-risk body regions, and derives the working postures that load them. Second, it experimentally evaluates commercially available wearable support devices in laboratory and field settings by measuring various performance metrics to classify each device by the level of support (LOS) it provides. Third, it develops a decision support system (DSS) that uses machine learning to predict each worker's required LOS across the three regions from anthropometric, demographic, and health-related data, and then selects, in a single optimization spanning all at-risk regions, the combination of support devices that best matches those predicted requirements under a single budget constraint. The DSS is subsequently validated through field implementation involving real-world mechanical-trades work. Experiments with 44 participants, including 13 practitioners, showed that all 12 devices received the same LOS classification in the laboratory and the field (mean absolute percentage difference of 4.83% in the LOS index). The machine learning models predicted required LOS with 10-fold cross-validated accuracies of 77.8%, 78.4%, and 79.0% for the back, legs, and shoulders, and the recommended devices were generally well accepted by three additional practitioners in the field validation.

Fourth, the applicability of the framework beyond the mechanical trades is established. The worker injury and working-posture analyses are repeated for four further construction trades, demonstrating that the framework’s targeted body regions and the postures under which its devices were evaluated recur beyond the trade group for which it was developed. These findings define the conditions under which the framework applies, with application to a trade that meets those conditions requiring repetition of the industry data collection component alone. The proposed framework offers a structured, systematic approach for matching individual mechanical-trade workers to the most suitable combination of wearable support devices for their specific needs. Because all at-risk body regions are optimized together rather than independently, the recommended set is tailored to the individual worker as a whole. The framework’s significance is twofold: for research, it places device support and worker need on a common scale, the LOS index; for practice, it provides occupational health or procurement staff with a DSS that can be operated without programming expertise. By providing objective, data-driven, and worker-tailored recommendations, this research has the potential to reduce the substantial lost-time and compensation-cost burden of WMSDs in the mechanical trades while supporting the retention of an experienced and difficult-to-replace skilled workforce.