Biodynamic Modeling and Analysis of Human-Exoskeleton Interactions in Simulated Patient Handling Tasks
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2025/10/13
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Description:Objective: To investigate the biodynamics of human-exoskeleton interactions during patient handling tasks using a subject-specific modeling approach. Background: Exoskeleton technology holds promise for mitigating musculoskeletal disorders caused by manual handling and most alarmingly by patient handling jobs. A deeper, more unified understanding of the biomechanical effects of exoskeleton use calls for advanced subject-specific models of complex, dynamic human-exoskeleton interactions. Methods: Twelve sex-balanced healthy participants performed three simulated patient handling tasks along with a reference load-lifting task, with and without wearing the exoskeleton, while their full-body motion and ground reaction forces were measured. Subject-specific models were constructed using motion and force data. Biodynamic response variables derived from the models were analyzed to examine the effects of the exoskeleton. Model validation used load-lifting trials with known hand forces. Results: The use of exoskeleton significantly reduced (19.7%-27.2%) the peak lumbar flexion moment but increased (26.4%-47.8%) the peak lumbar flexion motion, with greater moment percent reduction in more symmetric handling tasks; similarly affected the shoulder joint moments and motions but only during two more symmetric handling tasks; and significantly reduced the peak motions for the rest of the body joints. Conclusion: Subject-specific biodynamic models simulating exoskeleton-assisted patient handling were constructed and validated, demonstrating that the exoskeleton effectively lessened the peak loading to the lumbar and shoulder joints as prime movers while redistributing more motions to these joints and less to the remaining joints. Application: The findings offer new insights into biodynamic responses during exoskeleton-assisted patient handling, benefiting the development of more effective, possibly task- and individual-customized, exoskeletons. [Description provided by NIOSH]
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ISSN:0018-7208
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NIOSHTIC Number:nn:20070548
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Citation:Hum Factors 2025 Jan; :[Epub ahead of print]
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Contact Point Address:Xudong Zhang, Department of Industrial & Systems Engineering, Texas A&M University, 4077 Emerging Technologies Building, 3131 TAMU, College Station, TX 77843-3131, USA
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Email:xudongzhang@tamu.edu
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Federal Fiscal Year:2025
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Peer Reviewed:True
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Source Full Name:Human Factors
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Main Document Checksum:urn:sha-512:ff887032aa31b579b0f4de6fdc974eedbca467de1a57ec118a00d240037e1f47406726c60f3707bc3ddfe7a509efe6e86bf1c99d68935c6c89ec14c2dcb7b827
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