Abstract
Because of its simplicity, static optimization (SO) is frequently used to resolve the muscle redundancy problem (i.e., more muscles than degrees-of-freedom (DOF) in the human musculoskeletal system). However, SO minimizes antagonistic co-activation and likely joint stiffness as well, which may not be physiologically realistic since the body modulates joint stiffness during movements such as walking. Knowledge of joint stiffness is limited due to the difficulty of measuring it experimentally, leading researchers to estimate it using computational models. This study explores how imposing a synergy structure on the muscle activations estimated by optimization (termed “synergy optimization,” or SynO) affects calculated lower body joint stiffnesses during walking. By limiting the achievable muscle activations and coupling all time frames together, a synergy structure provides a potential mechanism for reducing indeterminacy and improving physiological co-activation but at the cost of a larger optimization problem. To compare joint stiffnesses produced by SynO (2–6 synergies) and SO, we used both approaches to estimate lower body muscle activations and forces for sample experimental overground walking data obtained from the first knee grand challenge competition. Both optimizations used a custom Hill-type muscle model that permitted analytic calculation of individual muscle contributions to the stiffness of spanned joints. Both approaches reproduced inverse dynamic joint moments well over the entire gait cycle, though SynO with only two synergies exhibited the largest errors. Maximum and mean joint stiffnesses for hip and knee flexion in particular decreased as the number of synergies increased from 2 to 6, with SO producing the lowest joint stiffness values. Our results suggest that SynO increases joint stiffness by increasing muscle co-activation, and furthermore, that walking with a reduced number of synergies may result in increased joint stiffness and perhaps stability.
Issue Section:
Research Papers
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