Towards Predictive Control of Trunk Internal Loads: Modeling Musculotendon Loads and Predicting Muscle Excitations Article Swipe
Related Concepts
Trunk
Control theory (sociology)
Internal model
Physical medicine and rehabilitation
Physics
Control (management)
Computer science
Medicine
Biology
Artificial intelligence
Ecology
Youfu Li
,
Massimo Sartori
,
Mahdi Nabipour
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/978-3-031-77588-8_32
· OA: W4407952894
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/978-3-031-77588-8_32
· OA: W4407952894
Low back pain is prevalent among industrial workers due to heavy lifting and poor posture, causing significant back injuries globally and affecting health and productivity. Existing back-support exoskeletons (BSEs) lack closed-loop control for musculotendon unit (MTU) or joint loads. This study proposes a framework for closed-loop control of L5/S1 joint loads, featuring a simplified BSE dynamics model, a muscle excitation predictor (MEP), and a nonlinear model predictive control (NMPC) algorithm. The simplified model matches the neuromusculoskeletal model with a correlation %0.98, and the MEP has a prediction accuracy of 0.86 ± 0.06. Future work will develop the MPC algorithm to finalize the control framework.
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