Development of a Muscle Model Parameter Calibration Method via Passive Muscle Force Minimization

Computational predictions of subject-specific muscle and knee joint contact forces during walking may improve individual rehabilitation treatment design. Such predictions depend directly on specified model parameter values. However, model parameters are difficult to measure non-invasively. Methods for muscle model parameter calibration have been developed previously. However, it is currently unknown how the musculoskeletal system chooses muscle model parameter values. Previous studies have hypothesized that muscles avoid injury during walking by generating little passive force and operating in the ascending region of the force-length curve. This hypothesis suggests that muscle model parameter values may be selected by the body to minimize passive force. The purpose of this study was to develop a method for calibrating muscle model parameter values and muscle moment arms during walking via minimization of passive force

Estimated Muscle Loads During Squat Exercise in Microgravity Conditions

Loss of muscle mass in microgravity is one of the primary factors limiting long-term space flight. NASA researchers have developed a number of exercise devices to address this problem. The most recent is the Advanced Resistive Exercise Device (ARED), which is currently used by astronauts on the International Space Station (ISS) to emulate typical free-weight exercises in microgravity. ARED exercise on the ISS is intended to reproduce Earth-level muscle loads, but the actual muscle loads produced remain unknown as they cannot currently be measured directly. In this study we estimated muscle loads experienced during squat exercise on ARED in microgravity conditions representative of Mars, the moon, and the ISS. The estimates were generated using a subject-specific musculoskeletal computer model and ARED exercise data collected on Earth. The results provide insight into the capabilities and limitations of the ARED machine

Personalized neuromusculoskeletal modeling to improve treatment of mobility impairments: a perspective from European research sites

Mobility impairments due to injury or disease have a significant impact on quality of life. Consequently, development of effective treatments to restore or replace lost function is an important societal challenge. In current clinical practice, a treatment plan is often selected from a standard menu of options rather than customized to the unique characteristics of the patient. Furthermore, the treatment selection process is normally based on subjective clinical experience rather than objective prediction of post-treatment function. The net result is treatment methods that are less effective than desired at restoring lost function. This paper discusses the possible use of personalized neuromusculoskeletal computer models to improve customization, objectivity, and ultimately effectiveness of treatments for mobility impairments. The discussion is based on information gathered from academic and industrial research sites throughout Europe, and both clinical and technical aspects of personalized neuromusculoskeletal modeling are explored. On the clinical front, we discuss the purpose and process of personalized neuromusculoskeletal modeling, the application of personalized models to clinical problems, and gaps in clinical application. On the technical front, we discuss current capabilities of personalized neuromusculoskeletal models along with technical gaps that limit future clinical application. We conclude by summarizing recommendations for future research efforts that would allow personalized neuromusculoskeletal models to make the greatest impact possible on treatment design for mobility impairments

Structures promoting research, training, and technology transfer in mobility: lessons learned from a visit to European centers

The purpose of this paper is to describe the education, research, technology transfer, and cooperative models that appear to have the greatest likelihood of successfully tackling the issue of technology to improve mobility. Ideally better models in each of these areas will lead to an increased number of researchers who are more productive. There will be increased international collaboration that will allow for better research with small and/or disadvantaged populations, and the research completed will lead to changes in clinical care that positively impact individuals with impair mobility

Muscle Synergies Facilitate Computational Prediction of Subject-Specific Walking Motions.

Researchers have explored a variety of neurorehabilitation approaches to restore normal walking function following a stroke. However, there is currently no objective means for prescribing and implementing treatments that are likely to maximize recovery of walking function for any particular patient. As a first step toward optimizing neurorehabilitation effectiveness, this study develops and evaluates a patient-specific synergy-controlled neuromusculoskeletal simulation framework that can predict walking motions for an individual post-stroke. The main question we addressed was whether driving a subject-specific neuromusculoskeletal model with muscle synergy controls (5 per leg) facilitates generation of accurate walking predictions compared to a model driven by muscle activation controls (35 per leg) or joint torque controls (5 per leg). To explore this question, we developed a subject-specific neuromusculoskeletal model of a single high-functioning hemiparetic subject using instrumented treadmill walking data collected at the subject's self-selected speed of 0.5 m/s. The model included subject-specific representations of lower-body kinematic structure, foot-ground contact behavior, electromyography-driven muscle force generation, and neural control limitations and remaining capabilities. Using direct collocation optimal control and the subject-specific model, we evaluated the ability of the three control approaches to predict the subject's walking kinematics and kinetics at two speeds (0.5 and 0.8 m/s) for which experimental data were available from the subject. We also evaluated whether synergy controls could predict a physically realistic gait period at one speed (1.1 m/s) for which no experimental data were available. All three control approaches predicted the subject's walking kinematics and kinetics (including ground reaction forces) well for the model calibration speed of 0.5 m/s. However, only activation and synergy controls could predict the subject's walking kinematics and kinetics well for the faster non-calibration speed of 0.8 m/s, with synergy controls predicting the new gait period the most accurately. When used to predict how the subject would walk at 1.1 m/s, synergy controls predicted a gait period close to that estimated from the linear relationship between gait speed and stride length. These findings suggest that our neuromusculoskeletal simulation framework may be able to bridge the gap between patient-specific muscle synergy information and resulting functional capabilities and limitations

Do Muscle Synergies Improve Optimization Prediction of Muscle Activations During Gait?

[Abstract]: Determination of muscle forces during motion can help to understand motor control, assess pathological movement, diagnose neuromuscular disorders, or estimate joint loads. Difficulty of in vivo measurement made computational analysis become a common alternative in which, as several muscles serve each degree of freedom, the muscle redundancy problem must be solved. Unlike static optimization (SO), synergy optimization (SynO) couples muscle activations across all time frames, thereby altering estimated muscle co-contraction. This study explores whether the use of a muscle synergy structure within an SO framework improves prediction of muscle activations during walking. A motion/force/electromyography (EMG) gait analysis was performed on five healthy subjects. A musculoskeletal model of the right leg actuated by 43 Hill-type muscles was scaled to each subject and used to calculate joint moments, muscle–tendon kinematics, and moment arms. Muscle activations were then estimated using SynO with two to six synergies and traditional SO, and these estimates were compared with EMG measurements. Synergy optimization neither improved SO prediction of experimental activation patterns nor provided SO exact matching of joint moments. Finally, synergy analysis was performed on SO estimated activations, being found that the reconstructed activations produced poor matching of experimental activations and joint moments. As conclusion, it can be said that, although SynO did not improve prediction of muscle activations during gait, its reduced dimensional control space could be beneficial for applications such as functional electrical stimulation or motion control and prediction.Ministerio de Ciencia, Innovación y Universidades; PGC2018-095145-B-I0

Evaluation of Different Optimal Control Problem Formulations for Solving the Muscle Redundancy Problem

This study evaluates several possible optimal control problem formulations for solving the muscle redundancy problem with the goal of identifying the most efficient and robust formulation. One novel formulation involves the introduction of additional controls that equal the time derivative of the states, resulting in very simple dynamic equations. The nonlinear equations describing muscle dynamics are then imposed as algebraic constraints in their implicit form, simplifying their evaluation. By comparing different problem formulations for computing muscle controls that can reproduce inverse dynamic joint torques during gait, we demonstrate the efficiency and robustness of the proposed novel formulation

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