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

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.National Institutes of Health (U.S.) (NIH Pioneer Award DP1 OD003646)Massachusetts Institute of Technology. Media Laboratory (Consortia Account 2736448)Massachusetts Institute of Technology. Media Laboratory (Consortia Account 6895867

Don't break a leg: Running birds from quail to ostrich prioritise leg safety and economy in uneven terrain

Cursorial ground birds are paragons of bipedal running that span a 500-fold mass range from quail to ostrich. Here we investigate the task-level control priorities of cursorial birds by analysing how they negotiate single-step obstacles that create a conflict between body stability (attenuating deviations in body motion) and consistent leg force–length dynamics (for economy and leg safety). We also test the hypothesis that control priorities shift between body stability and leg safety with increasing body size, reflecting use of active control to overcome size-related challenges. Weight-support demands lead to a shift towards straighter legs and stiffer steady gait with increasing body size, but it remains unknown whether non-steady locomotor priorities diverge with size. We found that all measured species used a consistent obstacle negotiation strategy, involving unsteady body dynamics to minimise fluctuations in leg posture and loading across multiple steps, not directly prioritising body stability. Peak leg forces remained remarkably consistent across obstacle terrain, within 0.35 body weights of level running for obstacle heights from 0.1 to 0.5 times leg length. All species used similar stance leg actuation patterns, involving asymmetric force–length trajectories and posture-dependent actuation to add or remove energy depending on landing conditions. We present a simple stance leg model that explains key features of avian bipedal locomotion, and suggests economy as a key priority on both level and uneven terrain. We suggest that running ground birds target the closely coupled priorities of economy and leg safety as the direct imperatives of control, with adequate stability achieved through appropriately tuned intrinsic dynamics

Don't break a leg: Running birds from quail to ostrich prioritise leg safety and economy in uneven terrain

Cursorial ground birds are paragons of bipedal running that span a 500-fold mass range from quail to ostrich. Here we investigate the task-level control priorities of cursorial birds by analysing how they negotiate single-step obstacles that create a conflict between body stability (attenuating deviations in body motion) and consistent leg force–length dynamics (for economy and leg safety). We also test the hypothesis that control priorities shift between body stability and leg safety with increasing body size, reflecting use of active control to overcome size-related challenges. Weight-support demands lead to a shift towards straighter legs and stiffer steady gait with increasing body size, but it remains unknown whether non-steady locomotor priorities diverge with size. We found that all measured species used a consistent obstacle negotiation strategy, involving unsteady body dynamics to minimise fluctuations in leg posture and loading across multiple steps, not directly prioritising body stability. Peak leg forces remained remarkably consistent across obstacle terrain, within 0.35 body weights of level running for obstacle heights from 0.1 to 0.5 times leg length. All species used similar stance leg actuation patterns, involving asymmetric force–length trajectories and posture-dependent actuation to add or remove energy depending on landing conditions. We present a simple stance leg model that explains key features of avian bipedal locomotion, and suggests economy as a key priority on both level and uneven terrain. We suggest that running ground birds target the closely coupled priorities of economy and leg safety as the direct imperatives of control, with adequate stability achieved through appropriately tuned intrinsic dynamics

Insights into muscle metabolic energetics: Modelling muscle-tendon mechanics and metabolic rates during walking across speeds

Prior studies have produced models to predict metabolic rates based on experimental observations of isolated muscle contraction from various species. Such models can provide reliable predictions of metabolic rates in humans if muscle properties and control are accurately modeled. This study aimed to examine how muscle-tendon model calibration and metabolic energy models influenced estimation of muscle-tendon states and time-series metabolic rates, to evaluate the agreement with empirical data, and to provide predictions of the metabolic rate of muscle groups and gait phases across walking speeds. Three-dimensional musculoskeletal simulations with prescribed kinematics and dynamics were performed. An optimal control formulation was used to compute muscle-tendon states with four levels of individualization, ranging from a scaled generic model and muscle controls based on minimal activations, to calibration of passive muscle forces, personalization of Achilles and quadriceps tendon stiffnesses, to finally informing muscle controls with electromyography. We computed metabolic rates based on existing models. Simulations with calibrated passive forces and personalized tendon stiffness most accurately estimate muscle excitations and fiber lengths. Interestingly, the inclusion of electromyography did not improve our estimates. The whole-body average metabolic cost was better estimated using Bhargava et al. 2004 and Umberger 2010 models. We estimated metabolic rate peaks near early stance, pre-swing, and initial swing at all walking speeds. Plantarflexors accounted for the highest cost among muscle groups at the preferred speed and was similar to the cost of hip adductors and abductors combined. Also, the swing phase accounted for slightly more than one-quarter of the total cost in a gait cycle, and its relative cost decreased with walking speed.Comment: 33 pages, 7 figure

Differences between joint-space and musculoskeletal estimations of metabolic rate time profiles

Motion capture laboratories can measure multiple variables at high frame rates, but we can only measure the average metabolic rate of a stride using respiratory measurements. Biomechanical simulations with equations for calculating metabolic rate can estimate the time profile of metabolic rate within the stride cycle. A variety of methods and metabolic equations have been proposed, including metabolic time profile estimations based on joint parameters. It is unclear whether differences in estimations are due to differences in experimental data or due to methodological differences. This study aimed to compare two methods for estimating the time profile of metabolic rate, within a single dataset. Knowledge about the consistency of different methods could be useful for applications such as detecting which part of the gait cycle causes increased metabolic cost in patients. Here we compare estimations of metabolic rate time profiles using a musculoskeletal and a joint-space method. The musculoskeletal method was driven by kinematics and electromyography data and used muscle metabolic rate equations, whereas the joint-space method used metabolic rate equations based on joint parameters. Both estimations of changes in stride average metabolic rate correlated significantly with large changes in indirect calorimetry from walking on different grades showing that both methods accurately track changes. However, estimations of changes in stride average metabolic rate did not correlate significantly with more subtle changes in indirect calorimetry due to walking with different shoe inclinations, and both the musculoskeletal and joint-space time profile estimations did not correlate significantly with each other except in the most downward shoe inclination. Estimations of the relative cost of stance and swing matched well with previous simulations with similar methods and estimations from experimental perturbations. Rich experimental datasets could further advance time profile estimations. This knowledge could be useful to develop therapies and assistive devices that target the least metabolically economic part of the gait cycle

Principles of energy optimization underlying human walking gait adaptations

Learning to move in novel situations is a complex process. We need to continually learn the changing situations and determine the best way to move. Optimization is a widely accepted framework for this process. However, little is known about algorithms used by the nervous system to perform this optimization. Our lab recently found evidence that people can continuously optimize energy during walking. My goal in this thesis is to identify principles of optimization, particularly energy optimization in walking, that govern our choice of movement in novel situations. I used two novel walking tasks for this purpose. For the first task, I designed, built, and tested a mechatronic system that can quickly, accurately, and precisely apply forces to a user’s torso. It changes the relationship between a walking gait and its associated energetic cost—cost landscape—to shift the energy optimal walking gait. Participants shift their gait towards the new optimum in these landscapes. In my second project, I aimed to understand how the nervous system identifies when to initiate optimization. I used my system to create cost landscapes of three different cost gradients. I found that experiencing a steeper cost gradient through natural variability is not sufficient to cue the nervous system to initiate optimization. For my third and fourth projects, I used the task of split-belt walking. I collaborated with another research group to analyse the mechanics and energetics of walking with different step lengths on a split-belt treadmill. I found that people can harness energy from a split-belt treadmill by placing their leading leg further forward on the fast belt, and that there may be an energy optimal gait. In my fourth project, I used computer modelling to identify that there may exist an energy optimal gait due to the trade-off between the cost of swinging the leg and the cost of redirecting the body center of mass when transitioning from step to step. Together, these projects develop a new system and a new approach to understand energy optimization in walking. They uncover principles governing the initiation of this process and our ability to benefit from it

Metabolically efficient walking assistance using optimized timed forces at the waist

The metabolic rate of walking can be reduced by applying a constant forward force at the center of mass. It has been shown that the metabolically optimal constant force magnitude minimizes propulsion ground reaction force at the expense of increased braking. This led to the hypothesis that selectively assisting propulsion could lead to greater benefits. We used a robotic waist tether to evaluate the effects of forward forces with different timings and magnitudes. Here, we show that it is possible to reduce the metabolic rate of healthy participants by 48% with a greater efficiency ratio of metabolic cost reduction per unit of net aiding work compared with other assistive robots. This result was obtained using a sinusoidal force profile with peak timing during the middle of the double support. The same timing could also reduce the metabolic rate in patients with peripheral artery disease. A model explains that the optimal force profile accelerates the center of mass into the inverted pendulum movement during single support. Contrary to the hypothesis, the optimal force timing did not entirely coincide with propulsion. Within the field of wearable robotics, there is a trend to use devices to mimic biological torque or force profiles. Such bioinspired actuation can have relevant benefits; however, our results demonstrate that this is not necessarily optimal for reducing metabolic rate

External work is deficient in both limbs of patients with unilateral PAD

External work was utilized to measure differences between the unaffected and the affected limb in patients with unilateral peripheral arterial disease compared to healthy controls. Patients with unilateral peripheral arterial disease have shown deficits in peak joint powers during walking in the unaffected and affected legs. However, no research has detailed the amount of work that is being performed by each leg compared to healthy controls even though such an analysis would provide valuable information on the energy output from the affected and the unaffected legs. Two hypotheses were tested: a) the unaffected and affected leg would perform less work than healthy controls in a pain-free state, and b) the onset of symptomatic claudication pain would result in further changes in the external work. Results showed that during a pain-free state, both the unaffected and affected legs perform less work than the healthy controls. After onset of claudication pain, the work output by the affected limb becomes further decreased while the unaffected limb experiences changes in negative external work. These findings combined with recent evidence of decreased peak powers in both legs in unilateral peripheral arterial disease patients reflects altered pathomechanics in both limbs compared to healthy controls

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

The role of muscle tendon unit elasticity in real life activities.

The interaction of a muscle and associated tendon during dynamic activities such as locomotion is critical for both force production and economical movement. It is generally assumed that, under sub-maximal conditions, muscle activation patterns are optimised to achieve maximum efficiency of work. Here, I explore the interaction between the contractile component (CC) and the elastic tendinous tissue to understand the relationship between a muscle's power output and efficiency. In this thesis, I examine the interaction of the CE and the elastic tendinous tissue and its effect on power output and efficiency of muscle using both experimental and modelling techniques. In the first chapter, a model of muscle energetics is developed and validated against dynamic muscle contractions of different muscle types. I then used this model to explore how optimal muscle power and efficiency varies with different activation conditions, clastic properties and length change trajectories. The third and forth chapter presents experiments which explore ultrasound measurement techniques for determining the length changes and mechanical properties of the human gastrocnemius medial is (GM) muscle fibres and Achilles tendon (AT) respectively. I then used similar techniques to explore musclc-tcndon unit (MTU) interaction during gait under different gait conditions. Specifically, I explore how GM power output and efficiency vary with different speeds and inclination and explore how variation in tendinous compliance might influence muscle efficiency. The results suggest that muscles remain highly efficient due to compliant tendons allowing muscle fibres to act at highly powerful and efficient velocities. However variation in power output and particularly muscle function affects the efficiency of muscle. Finally, I determined that the optimal value of tendon stiffness for maximum GM efficiency during walking and running is close to that determined experimentally