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Free, publicly-accessible full text available September 1, 2026
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Machine Learning-Based Fall Risk Detection in Human-Exoskeleton Interaction for Construction WorkersFree, publicly-accessible full text available January 1, 2026
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Free, publicly-accessible full text available January 1, 2026
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Free, publicly-accessible full text available December 1, 2025
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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.more » « less
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