A Quick Turn of Foot: Rigid Foot-Ground Contact Models for Human Motion Prediction

The effect of including a mobile arch, toe joint, and joint coupling on predictive neuromuscular simulations of human walking

Predictive neuromuscular simulations are a powerful tool for studying the biomechanics of human walking, and deriving design criteria for technical devices like prostheses or biorobots. Good agreement between simulation and human data is essential for transferability to the real world. The human foot is often modeled with a single rigid element, but knowledge of how the foot model affects gait prediction is limited. Standardized procedures for selecting appropriate foot models are lacking. We performed 2D predictive neuromuscular simulations with six different foot models of increasing complexity to answer two questions: What is the effect of a mobile arch, a toe joint, and the coupling of toe and arch motion through the plantar fascia on gait prediction? and How much of the foot's anatomy do we need to model to predict sagittal plane walking kinematics and kinetics in good agreement with human data? We found that the foot model had a significant impact on ankle kinematics during terminal stance, push-off, and toe and arch kinematics. When focusing only on hip and knee kinematics, rigid foot models are sufficient. We hope our findings will help guide the community in modeling the human foot according to specific research goals and improve neuromuscular simulation accuracy.

Rethinking Exoskeleton Simulation-Based Design: The Effect of Using Different Cost Functions

Designing an exoskeleton that can improve user capabilities is a challenging task, and most designs rely on experiments to achieve this goal. A different approach is to use simulation-based designs to determine optimal device parameters. Most of these simulations use full trajectory tracking limb kinematics during a natural gait as a reference. However, exoskeletons typically change the natural gait kinematics of the user. Other types of simulations assume that human gait is optimized for a cost function that combines several objectives, such as the cost of transport, injury prevention, and stabilization. In this study, we use a 2D OpenSim model consisting of 10 degrees of freedom and considering 18 muscles, together with the Moco optimization tool, to investigate the differences between these two approaches with respect to running with a passive knee exoskeleton. Utilizing this model, we test the effect of a full trajectory tracking objective with different weights (representing the importance of the objective in the optimization cost function) and show that when using weights that are typically used in the literature, there is no deviation from the experimental data. Next, we develop a multi-objective cost function with foot clearance term based on peak knee angle during swing, that achieves trajectories similar (RMSE=7.4 deg) to experimental running data. Finally, we investigate the effect of different parameters in the design of a clutch-based passive knee exoskeleton (1.5 kg at each leg) and find that a design that utilizes a 2.5 Nm/deg spring achieves an improvement of up to 8% in net metabolic energy. Our results show that tracking objectives in the cost function, even with a low weight, hinders the simulation's ability to change the gait trajectory. Thus, there is a need for other predictive simulation methods for exoskeletons.

Modeling foot rockers via functional calibration for use in clinical gait analysis

BACKGROUND

Goal of this work is a quantitative description of Jacquelin Perry's rocker concept by locating the position of the heel rocker and the forefoot rocker within segments of the foot via functional calibration.

METHODS

Two functional calibration tasks with the foot in ground contact were performed by ten typical developed adults and foot marker motion was captured. After applying a least-square method for constructing foot segments, their motion relative to the floor was analyzed via a functional algorithm. Resulting reference positions - namely the heel rotation center and the metatarsal rotation axis - were calculated. Further, the repeatability of the method and variability of outcome within the cohort was tested.

RESULTS

The heel rotation center is located substantially posterior (25 mm) and slightly more inferior (5 mm). to the midpoint of the two markers placed medially and laterally on the calcaneus. Repeated measures reveal a variation of this location around 5 mm. The forefoot center is slightly more medial to the "toe marker" (DMT2) and substantially more inferior (19 mm). The metatarsal rotation axis is slightly tilted in the frontal and transverse plane against the metatarsal line given between markers on MT1 and MT5 with small variation in repeated measures (1-2°).

SIGNIFICANCE

The determination of heel rotation center and the metatarsal rotation axis relative to foot segments can be determined with good repeatability and their location meet the intuitive expectation. Since they have a direct biomechanical meaning in the foot roll-over process in gait, they may be used for a more functionally oriented definition of foot segments potentially improving the calculation of foot kinematics and kinetics in future work.

Modeling Running via Optimal Control for Shoe Design

Shoe manufacturing technology is advancing faster than new shoe designs can viably be evaluated in human subject trials. To aid in the design process, this paper presents a model for estimating how new shoe properties will affect runner performance. This model assumes runners choose their gaits to optimize an intrinsic, unknown objective function. To learn this objective function, a simple two-dimensional mechanical model of runners was used to predict their gaits under different objectives, and the resulting gaits were compared to data from real running trials. The most realistic model gaits, i.e., the ones that best matched the data, were obtained when the model runners minimized the impulse they experience from the ground as well as the mechanical work done by their leg muscles. Using this objective function, the gait and thus performance of running under different shoe conditions can be predicted. The simple model is sufficiently sensitive to predict the difference in performance of shoes with disruptive designs but cannot distinguish between existing shoes whose properties are fairly similar. This model therefore is a viable tool for coarse-grain exploration of the design space and identifying promising behaviors of truly novel shoe materials and designs.

Predictive Simulations to Replicate Human Gait Adaptations and Energetics With Exoskeletons

Robotic exoskeletons have the potential to restore and enhance human mobility. However, optimally controlling these devices, to work in concert with human users, is challenging. Accurate model simulations of the interaction between exoskeletons and users may expedite the design process and improve control. Here, as a proof of principle, we tested if we could use predictive simulations to replicate human gait adaptations and changes in energy expenditure from an experiment where participants walked with exoskeletons. We recreated a past experimental paradigm, where robotic exoskeletons were used to shift people's energetically optimal step frequency to frequencies higher and lower than normally preferred. To match the experimental controller, we modelled knee-worn exoskeletons that applied resistive torques, either proportional or inversely proportional to step frequency-decreasing or increasing the energy optimal step frequency, respectively. We were able to replicate the experiment, finding higher and lower optimal step frequencies than in natural walking under each respective condition. Our simulated resistive torques and objective landscapes resembled the measured experimental resistive torque and energy landscapes. Individual muscle energetics revealed distinct coordination strategies consistent with each exoskeleton controller condition. Increasing the accuracy of step frequency and energetic predictions was best achieved by increasing the number of virtual participants (varying whole-body anthropometrics), rather than the number of muscle parameter sets (varying muscle anthropometrics). In future, our approach can be used to design controllers in advance of human testing, to help identify reasonable solution spaces or tailor design to individual users.