Synergy-Based Two-Level Optimization for Predicting Knee Contact Forces during Walking

Musculoskeletal models and optimization methods are combined to calculate muscle forces. Some model parameters cannot be experimentally measured due to the invasiveness, such as the muscle moment arms or the muscle and tendon lengths. Moreover, other parameters used in the optimization, such as the muscle synergy components, can be also unknown. The estimation of all these parameters needs to be validated to obtain physiologically consistent results. In this study, a two-step optimization problem was formulated to predict both muscle and knee contact forces of a subject wearing an instrumented knee prosthesis. In the outer level, muscle parameters were calibrated, whereas in the inner level, muscle activations were predicted. Two approaches are presented. In Approach A, contact forces were used when calibrating the parameters, whereas in Approach B, no contact force information was used as input. The optimization formulation is validated comparing the model and the experimental knee contact forces. The goal was to evaluate whether we can predict the contact forces when in-vivo contact forces are not available

The Influence of Neuromusculoskeletal Model Calibration Method on Predicted Knee Contact Forces during Walking

This study explored the influence of three model calibration methods on predicted knee contact and leg muscle forces during walking. Static optimization was used to calculate muscle activations for all three methods. Approach A used muscle-tendon model parameter values (i.e., optimal muscle fiber lengths and tendon slack lengths) taken directly from literature. Approach B used a simple algorithm to calibrate muscle-tendon model parameter values such that each muscle operated within the ascending region of its normalized force-length curve. Approach C used a novel two-level optimization procedure to calibrate muscle-tendon, moment arm, and neural control model parameter values while simultaneously predicting muscle activations

Optimization Problem Formulation for Predicting Knee Muscle and Contact Forces during Gait

The human body has more muscles than degrees of freedom (DOF), which leads to indeterminacy in the muscle force calculation. In this study, an optimization problem to estimate the lower-limb muscle forces during a gait cycle of a patient wearing an instrumented knee prosthesis is formulated. It consists of simulating muscle excitations in a physiological way while muscle parameters are calibrated

Optimal control prediction of a dynamically consistent walking motion for a spinal cord-injured subject assisted by orthoses

Postprint (published version

Formulation to Predict Lower Limb Muscle Forces during Gait

The human body has more muscles than Degrees of Freedom (DoF), and that leads to indeterminacy in the muscle force calculation. This study proposes the formulation of an optimization problem to estimate the lower-limb muscle forces during a gait cycle of a patient wearing an instrumented knee prosthesis. The originality of that formulation consists of simulating muscle excitations in a physiological way while muscle parameters are calibrated. Two approaches have been considered. In Approach A, measured contact forces are applied to the model and all inverse dynamics loads are matched in order to get a physiological calibration of muscle parameters. In Approach B, only the inverse dynamics loads not affected by the knee contact loads are matched. With that approach, contact forces can be predicted and validated by comparison with the experimental ones. Approach B is a test of the optimization method and it can be used for cases where no knee contact forces are available

Load assessment and analysis of impacts in multibody systems

The evaluation of contact forces during an impact requires the use of continuous force-based methods. An accurate prediction of the impact force demands the identification of the contact parameters on a case-by-case basis. In this paper, the preimpact effective kinetic energy (Formula presented.) is put forward as an indicator of the intensity of the impact force along the contact normal direction. This represents a part of the total kinetic energy of the system that is associated with the subspace of constrained motion defined by the impact constraints at the moment of contact onset. Its value depends only on the mechanical parameters and the configuration of the system. We illustrate in this paper that this indicator can be used to characterize the impact force intensity. The suitability of this indicator is confirmed by numerical simulations and experimentsPostprint (author's final draft

Neuromusculoskeletal Model Calibration Significantly Affects Predicted Knee Contact Forces for Walking

Though walking impairments are prevalent in society, clinical treatments are often ineffective at restoring lost function. For this reason, researchers have begun to explore the use of patient-specific computational walking models to develop more effective treatments. However, the accuracy with which models can predict internal body forces in muscles and across joints depends on how well relevant model parameter values can be calibrated for the patient. This study investigated how knowledge of internal knee contact forces affects calibration of neuromusculoskeletal model parameter values and subsequent prediction of internal knee contact and leg muscle forces during walking. Model calibration was performed using a novel two-level optimization procedure applied to six normal walking trials from the Fourth Grand Challenge Competition to Predict In Vivo Knee Loads. The outer-level optimization adjusted time-invariant model parameter values to minimize passive muscle forces, reserve actuator moments, and model parameter value changes with (Approach A) and without (Approach B) tracking of experimental knee contact forces. Using the current guess for model parameter values but no knee contact force information, the inner-level optimization predicted time-varying muscle activations that were close to experimental muscle synergy patterns and consistent with the experimental inverse dynamic loads (both approaches). For all the six gait trials, Approach A predicted knee contact forces with high accuracy for both compartments (average correlation coefficient r ¼ 0.99 and root mean square error (RMSE) ¼ 52.6 N medial; average r ¼ 0.95 and RMSE ¼ 56.6 N lateral). In contrast, Approach B overpredicted contact force magnitude for both compartments (average RMSE ¼ 323 N medial and 348 N lateral) and poorly matched contact force shape for the lateral compartment (average r ¼ 0.90 medial and À0.10 lateral). Approach B had statistically higher lateral muscle forces and lateral optimal muscle fiber lengths but lower medial, central, and lateral normalized muscle fiber lengths compared to Approach A. These findings suggest that poorly calibrated model parameter values may be a major factor limiting the ability of neuromusculoskeletal models to predict knee contact and leg muscle forces accurately for walking

The influence of neuromusculoskeletal model calibration method on predicted knee contact forces during walking

Neuromusculoskeletal models used to predict muscle and joint contact forces for a specific individual require specification of muscle-tendon, skeletal geometry, and neural control model parameter values. Though these parameter values should ideally be calibrated using in vivo data collected from the subject, they are often taken from generic models. This study explored the influence of three model calibration methods on predicted lower limb muscle and knee contact forces during walking. The calibrated model from each approach was used in a static optimization that predicted knee contact forces for six walking trials. The predictions were evaluated using knee contact forces measured in vivo from a subject implanted with a force-measuring knee replacement. The first calibration approach used muscle-tendon model parameter values (i.e., optimal muscle fiber lengths and tendon slack lengths) taken directly from the literature. The second approach calibrated muscle-tendon model parameter values such that each muscle operated within a physiological range on the ascending region of its normalized force-length curve. The third approach used a novel two-level optimization that exploited knowledge of the knee contact force measurements to calibrate muscle-tendon, moment arm, and neural control model parameter values such that the calibrated model would predict the in vivo contact forces as closely as possible. For the third approach, three walking trials were used to calibrate the model and the remaining three to test the calibrated model. Overall, calibration method had a large affect on predicted knee contact forces. The first method produced highly inaccurate contact force predictions and infeasible solutions for most time frames. The second approach produced accurate medial contact force predictions (average R2 = 0.89, average RMS error = 107 N) but inaccurate lateral predictions (average R2 = -1.77, average RMS error = 297 N). The third approach produced accurate testing predictions for both medial (average R2 = 0.91, average RMS error = 96 N) and lateral (average R2 = 0.76, average RMS error = 84 N) contact force. These results reveal that when knee contact force data are available, a single set of model parameter values can be successfully calibrated to predict medial and lateral knee contact force accurately over multiple walking cycles. They also reveal that when knee contact force data are not available (the most common situation), a simple calibration method based on muscle operating ranges on their normalized force-length curves may be sufficient to produce accurate medial but not lateral knee contact force predictions.Postprint (published version

Adaptation Strategies for Personalized Gait Neuroprosthetics

Personalization of gait neuroprosthetics is paramount to ensure their efficacy for users, who experience severe limitations in mobility without an assistive device. Our goal is to develop assistive devices that collaborate with and are tailored to their users, while allowing them to use as much of their existing capabilities as possible. Currently, personalization of devices is challenging, and technological advances are required to achieve this goal. Therefore, this paper presents an overview of challenges and research directions regarding an interface with the peripheral nervous system, an interface with the central nervous system, and the requirements of interface computing architectures. The interface should be modular and adaptable, such that it can provide assistance where it is needed. Novel data processing technology should be developed to allow for real-time processing while accounting for signal variations in the human. Personalized biomechanical models and simulation techniques should be developed to predict assisted walking motions and interactions between the user and the device. Furthermore, the advantages of interfacing with both the brain and the spinal cord or the periphery should be further explored. Technological advances of interface computing architecture should focus on learning on the chip to achieve further personalization. Furthermore, energy consumption should be low to allow for longer use of the neuroprosthesis. In-memory processing combined with resistive random access memory is a promising technology for both. This paper discusses the aforementioned aspects to highlight new directions for future research in gait neuroprosthetics.Peer ReviewedPostprint (published version