Human Leg Model Predicts Ankle Muscle-Tendon Morphology, State, Roles and Energetics in Walking

Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
PLoS Computational Biology (Impact Factor: 4.62). 03/2011; 7(3):e1001107. DOI: 10.1371/journal.pcbi.1001107
Source: PubMed


A common feature in biological neuromuscular systems is the redundancy in joint actuation. Understanding how these redundancies are resolved in typical joint movements has been a long-standing problem in biomechanics, neuroscience and prosthetics. Many empirical studies have uncovered neural, mechanical and energetic aspects of how humans resolve these degrees of freedom to actuate leg joints for common tasks like walking. However, a unifying theoretical framework that explains the many independent empirical observations and predicts individual muscle and tendon contributions to joint actuation is yet to be established. Here we develop a computational framework to address how the ankle joint actuation problem is resolved by the neuromuscular system in walking. Our framework is founded upon the proposal that a consideration of both neural control and leg muscle-tendon morphology is critical to obtain predictive, mechanistic insight into individual muscle and tendon contributions to joint actuation. We examine kinetic, kinematic and electromyographic data from healthy walking subjects to find that human leg muscle-tendon morphology and neural activations enable a metabolically optimal realization of biological ankle mechanics in walking. This optimal realization (a) corresponds to independent empirical observations of operation and performance of the soleus and gastrocnemius muscles, (b) gives rise to an efficient load-sharing amongst ankle muscle-tendon units and (c) causes soleus and gastrocnemius muscle fibers to take on distinct mechanical roles of force generation and power production at the end of stance phase in walking. The framework outlined here suggests that the dynamical interplay between leg structure and neural control may be key to the high walking economy of humans, and has implications as a means to obtain insight into empirically inaccessible features of individual muscle and tendons in biomechanical tasks.

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Available from: Pavitra Krishnaswamy, Nov 04, 2014
    • "This requires a musculoskeletal modeling method that accounts for physiological muscle recruitment strategies and contraction dynamics. In this study we address this challenge by using an EMG-driven musculoskeletal model of the human leg (Lloyd and Besier, 2003; Buchanan et al., 2004; Krishnaswamy et al., 2011; Sartori et al., 2012b; Farris et al., 2014). The term " leg " is used throughout this manuscript according to its anatomical definition. "
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    ABSTRACT: This work presents an electrophysiologically and dynamically consistent musculoskeletal model to predict stiffness in the human ankle and knee joints as derived from the joints constituent biological tissues, i.e. the spanning musculotendon units. The modeling method we propose uses electromyograms (EMG) recorded from 13 muscle groups to drive forward dynamic simulations of the human leg for five healthy subjects during over-ground walking and running. The EMG-driven musculoskeletal model estimates musculotendon and resulting joint stiffness that is consistent with experimental EMG data as well as with the experimental joint moments. This provides a framework that allows for the first time observing (1) the elastic interplay between the knee and ankle joints (2) the individual muscle contribution to joint stiffness, and (3) the underlying co-contraction strategies. It provides a theoretical description of how stiffness modulates as a function of muscle activation, fiber contraction, and interacting tendon dynamics. Furthermore, it describes how this differs from currently available stiffness definitions including quasi-stiffness and short-range stiffness. This work offers a theoretical and computational basis for describing and investigating the neuromuscular mechanisms underlying human locomotion. Copyright © 2014, Journal of Neurophysiology.
    No preview · Article · Aug 2015 · Journal of Neurophysiology
    • "To assess metabolic performance of different control strategies we calculated average metabolic rate, and associated apparent MTU and CE efficiencies. Metabolic rate was determined based on CE velocity, and scaled by activation, using a model taken from (Krishnaswamy et al., 2011)P met ðtÞ ¼ pðv CE ðtÞÞ Â αðtÞ Â jF max  v max j where P met ðtÞ is instantaneous metabolic rate at time t, and pðv CE ðtÞÞ is a cost function taken from (Alexander, 1997;Ma and Zahalak, 1991) and shown in Table 2. Average metabolic rate for each condition was computed by integrating instantaneous metabolic cost for each period, multiplying by frequency (i.e. dividing by period), and dividing by the system mass M (Table 1) to get units of units of W/kg: "

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    • "Subsequently , EMG-linear envelopes have been used to drive neuromusculoskeletal models (i.e. EMG-driven modeling) during a variety of dynamic motor tasks and predict resulting joint moments (Besier et al., 2009; Krishnaswamy et al., 2011; Manal et al., 2002; Sartori et al., 2012a). In these, the underlying musculoskeletal model is scaled and calibrated to an individual's anthropometry and EMG-force generating properties. "
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    ABSTRACT: Current electromyography (EMG)-driven musculoskeletal models are used to estimate joint moments measured from an individual's extremities during dynamic movement with varying levels of accuracy. The main benefit is the underlying musculoskeletal dynamics is simulated as a function of realistic, subject-specific, neural-excitation patterns provided by the EMG data. The main disadvantage is surface EMG cannot provide information on deeply located muscles. Furthermore, EMG data may be affected by cross-talk, recording and post-processing artifacts that could adversely influence the EMG's information content. This limits the EMG-driven model's ability to calculate the multi-muscle dynamics and the resulting joint moments about multiple degrees of freedom. We present a hybrid neuromusculoskeletal model that combines calibration, subject-specificity, EMG-driven and static optimization methods together. In this, the joint moment tracking errors are minimized by balancing the information content extracted from the experimental EMG data and from that generated by a static optimization method. Using movement data from five healthy male subjects during walking and running we explored the hybrid model's best configuration to minimally adjust recorded EMGs and predict missing EMGs while attaining the best tracking of joint moments. Minimally adjusted and predicted excitations substantially improved the experimental joint moment tracking accuracy than current EMG-driven models. The ability of the hybrid model to predict missing muscle EMGs was also examined. The proposed hybrid model enables muscle-driven simulations of human movement while enforcing physiological constraints on muscle excitation patterns. This might have important implications for studying pathological movement for which EMG recordings are limited.
    Full-text · Article · Nov 2014 · Journal of Biomechanics
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