Many individuals with lower limb amputations or neuromuscular impairments face mobility challenges attributable to suboptimal assistive device design. Forward dynamic modeling and simulation of human walking using conventional biomechanical gait models offer an alternative to intuition-based assistive device design, providing insight into the biomechanics underlying pathological gait. Musculoskeletal models enable better understanding of prosthesis and/or exoskeleton contributions to the human musculoskeletal system, and device and user contributions to both body support and propulsion during gait. This paper reviews current literature that have used forward dynamic simulation of clinical population musculoskeletal models to perform assistive device design optimization using optimal control, optimal tracking, computed muscle control (CMC) and reflex-based control. Musculoskeletal model complexity and assumptions inhibit forward dynamic musculoskeletal modeling in its current state, hindering computational assistive device design optimization. Future recommendations include validating musculoskeletal models and resultant assistive device designs, developing less computationally expensive forward dynamic musculoskeletal modeling methods, and developing more efficient patient-specific musculoskeletal model generation methods to enable personalized assistive device optimization.
Lower-limb amputees can suffer from preventable pain and bone disorders attributable to suboptimal prosthesis design. Predictive modelling and simulation of human walking using conventional biomechanical gait models offer an alternative to intuition-based prosthesis design, providing insight into the biomechanics underlying pathological gait. Musculoskeletal models additionally enable understanding of prosthesis contributions to the human musculoskeletal system, and both prosthesis and individual muscle contributions to body support and propulsion during gait. Based on this review, forward dynamic simulation of amputee musculoskeletal models have been used to perform prosthesis design optimization using optimal control and reflex-based control. Musculoskeletal model complexity and assumptions inhibit fully predictive musculoskeletal modelling in its current state, hindering computational prosthesis design optimization. Future recommendations include validating musculoskeletal models and resultant optimized prosthesis designs, developing less computationally-expensive predictive musculoskeletal modelling methods, and developing more efficient patient-specific musculoskeletal model generation methods to enable personalized prosthesis optimization.
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