A Muscle-Reflex Model That Encodes Principles of Legged Mechanics Produces Human Walking Dynamics and Muscle Activities

By Hartmut Geyer, Hugh M. Herr. IEEE transactions on neural systems and rehabilitation engineering, 2010

685 citations recorded by the source corpus.

While neuroscientists identify increasingly complex neural circuits that control animal and human gait, biomechanists find that locomotion requires little control if principles of legged mechanics are heeded that shape and exploit the dynamics of legged systems. Here, we show that muscle reflexes could be vital to link these two observations. We develop a model of human locomotion that is controlled by muscle reflexes which encode principles of legged mechanics. Equipped with this reflex control, we find this model to stabilize into a walking gait from its dynamic interplay with the ground, reproduce human walking dynamics and leg kinematics, tolerate ground disturbances, and adapt to slopes without parameter interventions. In addition, we find this model to predict some individual muscle activation patterns known from walking experiments. The results suggest not only that the interplay between mechanics and motor control is essential to human locomotion, but also that human motor output could for some muscles be dominated by neural circuits that encode principles of legged mechanics.

Subfields: Biomedical Engineering

View Paper: https://www.semanticscholar.org/paper/41670b2a4759c7ce009cb51d2d56649816637a2a

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In Biomedical Engineering

Citations in the corpus

Deep Kernels for Optimizing Locomotion Controllers (2017, methodology): “Our second set of experiments is on the Neuromuscular model [8].”
Combining the benefits of function approximation and trajectory optimization (2014, background): “Several hand-crafted locomotion laws have been proposed for human walking and running that match human observations [7].”
Toward fast policy search for learning legged locomotion (2012, background): “Neuromuscular legged systems [7] are clearly capable of using biomechanics that simplify the control problem such that a neural control system with its long delays (a signal”
Is my model good enough? Best practices for verification and validation of musculoskeletal models and simulations of movement. (2015, background): “Applying predictive simulation to locomotion is more challenging than tracking-based approaches and is also computationally expensive, but several recent studies show promise [89,139].”
Interactive Control of Diverse Complex Characters with Neural Networks (2015, methodology): “This model could be used to experiment with the effects of sensor delays and the resulting motions, for example [2].”