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

Use of performance indicators in the analysis of running gait impacts

[Summary] Foot-ground impact is a critical event during the running cycle. In this work, three performance indicators were used to characterize foot-ground impact intensity: the effective preimpact kinetic energy, representative elements of the effective mass matrix, and the critical coefficient of friction. These performance indicators can be obtained from the inertial properties of the biomechanical system and its pre-impact mechanical state, avoiding the need to carry out force measurements. Ground reaction forces and kinematic data were collected from the running motion of an adult that adopted both rear-foot and fore-foot strike patterns. Different running cycles were analysed and statistical tests performed. Results showed that the three proposed indicators are able to illustrate significant differences between fore-foot and rear-foot strike impacts. They also support the hypothesis that fore-foot strike reduces impact intensity. On the other hand, a higher likelihood of slipping during the contact onset is associated with fore-foot strike pattern.Ministerio de Economía y Competitividad (MINECO). JCI-2012-1237

Novel computational protocol to support transfemoral prosthetic alignment procedure using machine learning techniques

The prosthetic alignment procedure considers biomechanical, anatomical and comfort characteristics of the amputee to achieve an acceptable gait. Prosthetic malalignment induces long-term disease. The assessment of alignment is highly variable and subjective to the experience of the prosthetist, so the use of machine learning could assist the prosthetist during the judgment of optimal alignment.Peer ReviewedPostprint (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

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

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

Muscle parameter identification by using an artificially activated muscle model

Postprint (published version