Dataset Information

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.

SUBMITTER: Markowitz J

PROVIDER: S-EPMC4866735 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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Human Leg Model Predicts Muscle Forces, States, and Energetics during Walking.

Markowitz Jared J Herr Hugh H

PLoS computational biology 20160513 5

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 d ...[more]

PMID: 27175486

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Project description:Inverse dynamics problems are usually solved in the analysis of human gait to obtain reaction forces and moments at the joints. However, these actions are not the actual forces and moments supported by the joint structure, because they do not consider the forces of the muscles acting across the joint. Therefore, to analyse bone-on bone forces it is necessary to estimate those muscle forces. Usually, this problem is addressed by means of optimization algorithms. One of the parameters required to solve this problem is the musculotendon geometry. These data are usually taken from cadavers or MRI data from several subjects, different from the analysed subject. Then, the model is scaled to the subject morphology. This procedure constitutes a source of error. The goals of this work were two. First, to perform a sensitivity analysis of the influence of muscle insertion locations on the muscle forces acting on the hip joint and on the hip joint bone-on-bone forces. Second, to compare the hip joint bone-on-bone forces during gait cycle obtained through muscle insertion locations taken from a musculoskeletal model template and a scaling procedure to those obtained from a subject-specific model using an MRI of the subject. The problem was solved using OpenSim. Results showed that anatomical variability should be analysed from two perspectives. One the one hand, throughout the gait cycle, in a global way. On the other hand, at a characteristic instant of the gait cycle. Variations of ±1 cm in the position of the attachment points of certain muscles caused variations of up to 14.21% in averaged deviation of the muscle forces and 58.96% in the peak force in the modified muscle and variations up to 57.23% in the averaged deviation of the muscle force and up to 117.23% in the peak force in the rest of muscles. Then, the influence of that variability on muscle activity patterns and hip bone-on-bone forces could be described more precisely. A biomechanical analysis of a subject-specific musculoskeletal model was carried out. Using MRI data, variations up to 5 cm in the location of the insertion points were introduced. These modifications showed significant differences between the baseline model and the customized model: within the range [-12%, 10%] for muscle forces and around 35% of body weight for hip bone-on-bone forces.

Project description:BackgroundSuccessful walking requires the execution of the pre-swing biomechanical tasks of body propulsion and leg swing initiation, which are often impaired post-stroke. While excess rectus femoris activity during swing is often associated with low knee flexion, previous work has suggested that deficits in propulsion and leg swing initiation may also contribute. The purpose of this study was to determine underlying causes of propulsion, leg swing initiation and knee flexion deficits in pre-swing and their link to stiff knee gait in individuals post-stroke.MethodsMusculoskeletal models and forward dynamic simulations were developed for individuals post-stroke (n = 15) and healthy participants (n = 5). Linear regressions were used to evaluate the relationships between peak knee flexion, braking and propulsion symmetry, and individual muscle contributions to braking, propulsion, knee flexion in pre-swing, and leg swing initiation.ResultsFour out of fifteen of individuals post-stroke had higher plantarflexor contributions to propulsion and seven out of fifteen had higher vasti contributions to braking on their paretic leg relative to their nonparetic leg. Higher gastrocnemius contributions to propulsion predicted paretic propulsion symmetry (p = 0.005) while soleus contributions did not. Higher vasti contributions to braking in pre-swing predicted lower knee flexion (p = 0.022). The rectus femoris had minimal contributions to lower knee flexion acceleration in pre-swing compared to contributions from the vasti. However, for some individuals with low knee flexion, during pre-swing the rectus femoris absorbed more power and the iliopsoas contributed less power to the paretic leg. Total musculotendon work done on the paretic leg in pre-swing did not predict knee flexion during swing.ConclusionsThese results emphasize the multiple causes of propulsion asymmetry in individuals post-stroke, including low plantarflexor contributions to propulsion, increased vasti contributions to braking and reliance on compensatory mechanisms. The results also show that the rectus femoris is not a major contributor to knee flexion in pre-swing, but absorbs more power from the paretic leg in pre-swing in some individuals with stiff knee gait. These results highlight the need to identify individual causes of propulsion and knee flexion deficits to design more effective rehabilitation strategies.

Project description:Handholding can naturally occur between two walkers. When people walk side-by-side, either with or without hand contact, they often synchronize their steps. However, despite the importance of haptic interaction in general and the natural use of hand contact between humans during walking, few studies have investigated forces arising from physical interactions. Eight pairs of adult subjects participated in this study. They walked on side-by-side treadmills at 4 km/h independently and with hand contact. Only hand contact-related sensory information was available for unintentional synchronization, while visual and auditory communication was obstructed. Subjects walked at their natural cadences or following a metronome. Limb kinematics, hand contact 3D interaction forces and EMG activity of 12 upper limb muscles were recorded. Overall, unintentional step frequency locking was observed during about 40% of time in 88% of pairs walking with hand contact. On average, the amplitude of contact arm oscillations decreased while the contralateral (free) arm oscillated in the same way as during normal walking. Interestingly, EMG activity of the shoulder muscles of the contact arm did not decrease, and their synergistic pattern remained similar. The amplitude of interaction forces and of trunk oscillations was similar for synchronized and non-synchronized steps, though the synchronized steps were characterized by significantly more regular orientations of interaction forces. Our results further support the notion that gait synchronization during natural walking is common, and that it may occur through interaction forces. Conservation of the proximal muscle activity of the contact (not oscillating) arm is consistent with neural coupling between cervical and lumbosacral pattern generation circuitries ("quadrupedal" arm-leg coordination) during human gait. Overall, the findings suggest that individuals might integrate force interaction cues to communicate and coordinate steps during walking.

Dataset Information

Human Leg Model Predicts Muscle Forces, States, and Energetics during Walking.

Publications

Human Leg Model Predicts Muscle Forces, States, and Energetics during Walking.

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