User Assessment of Passive Exoskeleton in Manual Material Handling
Publication Date
1-1-2024
Document Type
Conference Proceeding
Publication Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
14709 LNCS
DOI
10.1007/978-3-031-61060-8_17
First Page
231
Last Page
242
Abstract
Manual material handling (MMH) tasks, involving activities like lifting, carrying, and holding various loads, contribute significantly to lower back fatigue, pain, injuries, and musculoskeletal disorders in occupational settings. To address these challenges, passive back-supporting industrial exoskeletons (BExo) have been introduced as assistive technologies to mitigate ergonomic risks associated with MMH tasks. This study evaluates the acceptance of BExos during MMH tasks in laboratory conditions, providing insights from users’ perspectives. The results, showing consistently reduced physical discomfort scores across tasks, emphasize the efficacy of the back-supporting exoskeleton in alleviating perceived effort during manual material handling. Notable reductions in discomfort, especially in critical areas like the lower back, upper back, shoulders, and knees during the box-carrying task, highlight the targeted impact of the exoskeleton on essential anatomical regions. Positive user perceptions underscore the utility of the exoskeleton in alleviating fatigue, promoting ergonomic posture, and serving as a comfortable and wearable assistive technology. These findings offer insights for safety practitioners and human factors experts, suggesting the potential applicability of the back exoskeleton in diverse occupational tasks and settings.
Keywords
Ergonomics, Exoskeleton, Fatigue, Safety, Users and Workplace
Department
Aviation and Technology
Recommended Citation
Arnold Nieto, Hardik Vora, Fatemeh Davoudi, and Armin Moghadam. "User Assessment of Passive Exoskeleton in Manual Material Handling" Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2024): 231-242. https://doi.org/10.1007/978-3-031-61060-8_17