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

Abstract

Physics-based predictive simulations have been shown to capture many salient features of human walking. Yet they often fail to produce realistic stance knee and ankle mechanics. While the influence of the performance criterion on the predicted walking pattern has been previously studied, the influence of musculoskeletal mechanics has been less explored. Here, we investigated the influence of two mechanical assumptions on the predicted walking pattern: the complexity of the foot model and the stiffness of the Achilles tendon. We found, through three-dimensional muscle-driven predictive simulations of walking, that modeling the toes, and thus using two-segment instead of single-segment foot models, contributed to robustly eliciting physiological stance knee flexion angles, knee extension torques, and knee extensor activity. Modeling toes also slightly decreased the first vertical ground reaction force peak, increasing its agreement with experimental data, and improved stance ankle kinetics. It nevertheless slightly worsened predictions of ankle kinematics. Decreasing Achilles tendon stiffness improved the realism of ankle kinematics, but there remain large discrepancies with experimental data. Overall, this simulation study shows that not only the performance criterion but also mechanical assumptions affect predictive simulations of walking. Improving the realism of predictive simulations is required for their application in clinical contexts. Here, we suggest that using more complex foot models might contribute to such realism.

Simulating the effect of ankle plantarflexion and inversion-eversion exoskeleton torques on center of mass kinematics during walking

“…the orientation of the metatarsophalangeal joint axis to be perpendicular to the sagittal plane and applied a linear rotational spring-damper force with a stiffness of 25.0 Nm rad -1 and a damping coefficient of 2 Nm s rad -1 [46]. Damping forces with a 2 Nm s rad -1 coefficient were also applied to the lumbar and subtalar joints, since the lumbar joint was not muscle-actuated and the subtalar joint did not track any experimental data (see "Normal walking simulations" below).…”

Prediction of walking kinematics and muscle activities under idealized lower limb exoskeleton assistances

“…Since the reference motion was also predicted with optimization from a 2D model (Falisse et al, 2019), the deviation is explained by Falisse et al as a result of model choices instead of local optima. Extending the model from 2D to 3D (Falisse et al, 2019) or adding toe degrees of freedom to the foot (Falisse et al, 2022) likely will improve the prediction outcomes. Since in our prediction method, tracking is a part of the objective function, we expect better joint motion prediction if real experimental data is used as the reference motion, which is a planned next step of this study.…”

“…Nonetheless, the single shooting method requires numerical integration of the dynamic equations to generate the motion trajectory and hence is computationally expensive (Dorn et al, 2015). In addition to single shooting, direct collocation, a direct optimal control method that relies on gradient-based optimization has been used for musculoskeletal motion tracking and prediction (Falisse et al, 2022;Febrer-Nafría et al, 2022;Nguyen et al, 2019;Park et al, 2022). The introduction of the opensource toolkit, OpenSim Moco (Dembia et al, 2020), has made direct collocation more accessible to the musculoskeletal biomechanics community.…”

OpenCap: 3D human movement dynamics from smartphone videos

“…Note that since no markers are attached to the toes, no reliable estimates of metatarsophalangeal joint kinematics can be obtained. The metatarsophalangeal joint is nevertheless included when generating tracking simulations, since modeling that joint improves knee mechanics in muscle-driven simulations 79 . The musculoskeletal model is driven by 80 muscles actuating the lower-limb coordinates, 13 ideal torque motors actuating the lumbar, shoulder, and elbow coordinates, and six contact spheres per foot modeling food-ground contacts 80,81 .…”