title: Human Leg Model Predicts Muscle Forces, States, and Energetics during Walking.
abstract: Humans employ a high degree of redundancy in joint actuation, with different combinations of muscle and tendon action providing the same net joint torque. Both the resolution of these redundancies and the energetics of such systems depend on the dynamic properties of muscles and tendons, particularly their force-length relations. Current walking models that use stock parameters when simulating muscle-tendon dynamics tend to significantly overestimate metabolic consumption, perhaps because they do not adequately consider the role of elasticity. As an alternative, we posit that the muscle-tendon morphology of the human leg has evolved to maximize the metabolic efficiency of walking at self-selected speed. We use a data-driven approach to evaluate this hypothesis, utilizing kinematic, kinetic, electromyographic (EMG), and metabolic data taken from five participants walking at self-selected speed. The kinematic and kinetic data are used to estimate muscle-tendon lengths, muscle moment arms, and joint moments while the EMG data are used to estimate muscle activations. For each subject we perform an optimization using prescribed skeletal kinematics, varying the parameters that govern the force-length curve of each tendon as well as the strength and optimal fiber length of each muscle while seeking to simultaneously minimize metabolic cost and maximize agreement with the estimated joint moments. We find that the metabolic cost of transport (MCOT) values of our participants may be correctly matched (on average 0.36±0.02 predicted, 0.35±0.02 measured) with acceptable joint torque fidelity through application of a single constraint to the muscle metabolic budget. The associated optimal muscle-tendon parameter sets allow us to estimate the forces and states of individual muscles, resolving redundancies in joint actuation and lending insight into the potential roles and control objectives of the muscles of the leg throughout the gait cycle.
authors.affiliation: MIT Media Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
authors.forename: Jared
authors.initials: J
authors.lastname: Markowitz
authors.affiliation: MIT Media Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
authors.forename: Hugh
authors.initials: H
authors.lastname: Herr
mesh_terms: D000328:Adult; D000465:Algorithms; D001499:Bayes Theorem; D001696:Biomechanical Phenomena; D019295:Computational Biology; D003198:Computer Simulation; D004734:Energy Metabolism; D005684:Gait / Q000502:physiology; D006801:Humans; D007866:Leg / Q000502:physiology; D008297:Male; D008954:Models, Biological; D009119:Muscle Contraction / Q000502:physiology; D018482:Muscle, Skeletal / Q000502:physiology; D013710:Tendons / Q000502:physiology; D019415:Torque; D016138:Walking / Q000502:physiology*; D055815:Young Adult