Ultrasound-Based Optimal Parameter Estimation Improves Assessment of Calf Muscle–Tendon Interaction During Walking

Abstract

We present and evaluate a new approach to estimate calf muscle-tendon parameters and calculate calf muscle-tendon function during walking. We used motion analysis, ultrasound, and EMG data of the calf muscles collected in six young and six older adults during treadmill walking as inputs to a new optimal estimation algorithm. We used estimated parameters or scaled generic parameters in an existing approach to calculate muscle fiber lengths and activations. We calculated the fit with experimental data in terms of root mean squared differences (RMSD) and coefficients of determination (R²). We also calculated the calf muscle metabolic energy cost. RMSD between measured and calculated fiber lengths and activations decreased and R² increased when estimating parameters compared to using scaled generic parameters. Moreover, R² between measured and calculated gastrocnemius medialis fiber length and soleus activations increased by 19 % and 70 %, and calf muscle metabolic energy decreased by 25% when using estimated parameters compared to using scaled generic parameters at speeds not used for estimation. This new approach estimates calf muscletendon parameters in good accordance with values reported in literature. The approach improves calculations of calf muscle-tendon interaction during walking and highlights the importance of individualizing calf muscle-tendon parameters.

Modeling toes contributes to realistic stance knee mechanics in three-dimensional predictive simulations of walking

“…If we overestimate the decrease in muscle-tendon lengths with ankle plantarflexion, this would result in reduced ankle plantarflexion in predictive simulations where having nearly isometric fiber lengths seems to optimize performance. This hypothesis is supported by previous results from inverse analysis (i.e., simulations for which the kinematics are prescribed) comparing simulated fiber lengths to their experimental counterparts measured using ultrasound [16]. In that study, we found that when imposing joint kinematics and kinetics, the simulated change in gastrocnemius fiber length during stance exceeded the measured change.…”

“…Since it is the interaction between ankle kinematics and Achilles tendon properties that determines plantarflexor operating length and velocity, and thereby efficiency, adjusting the Achilles tendon stiffness in our simulations that optimize efficiency might influence predicted ankle kinematics. Second, we previously found that reducing Achilles tendon stiffness in our models resulted in inverse dynamic estimates of gastrocnemius fiber length trajectories that were in closer agreement with fiber length trajectories measured using ultrasonography during walking [16] and running [17]. In inverse dynamic simulations of walking, we obtained the best agreement between simulated and measured fiber lengths when decreasing Achilles tendon stiffness by 60%.…”

Perspective on musculoskeletal modelling and predictive simulations of human movement to assess the neuromechanics of gait

“…This approach has been successfully applied to estimate properties of the ankle and knee actuators but the inability to collect surface EMG from deep muscles limits its use for the hip actuators. Similarly, dynamic ultrasound-based measures of fibre lengths can be used for parameter identification but such measures are typically limited to very few muscles [69].…”

In Silico-Enhanced Treatment and Rehabilitation Planning for Patients with Musculoskeletal Disorders: Can Musculoskeletal Modelling and Dynamic Simulations Really Impact Current Clinical Practice?

“…The collection of muscle strength data, via dynamometry allows for the customisation of the maximal force-generating capacity (i.e., maximal isometric force) of muscle groups. Further, these dynamometry data can be used to further refine parameters within the previously mentioned Hill-type muscle model [26][27][28][29]. Despite the vast number of parameters which can be personalised, the inclusion of the above personalised parameters is uncommon and building fully subject-specific MSK models is rare given the complexity of the additional data collection.…”