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Decoding the Neural Key to How Humans Efficiently Walk at Varied Speeds
- January 24, 2024
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We typically don't think about it whilst doing it, but walking is a complicated task. Controlled by our nervous system, our bones, joints, muscles, tendons, ligaments and other connective tissues (i.e., the musculoskeletal system) must move in coordination and respond to unexpected changes or disturbances at varying speeds in a highly efficient manner. Replicating this in robotic technologies is no small feat.
Now, a research group from Tohoku University Graduate School of Engineering has replicated human-like variable speed walking using a musculoskeletal model - one steered by a reflex control method reflective of the human nervous system. This breakthrough in biomechanics and robotics sets a new benchmark in understanding human movement and paves the way for innovative robotic technologies.
"Our study has tackled the intricate challenge of replicating efficient walking at various speeds - a cornerstone of the human walking mechanism," points out Associate Professor Dai Owaki, and co-author of the study along with Shunsuke Koseki and Professor Mitsuhiro Hayashibe. "These insights are pivotal in pushing the boundaries for understanding human locomotion, adaptation, and efficiency."
The achievement was thanks to an innovative algorithm. The algorithm evolved beyond the conventional least squares method and helped devise a neural circuit model optimized for energy efficiency over diverse walking speeds. Intensive analysis of these neural circuits, particularly those controlling the muscles in the leg swing phase, unveiled critical elements of energy-saving walking strategies. These revelations enhance our grasp of the complex neural network mechanisms that underpin human gait and its effectiveness.
Such developments could improve mobility solutions for individuals with disabilities and advance robotic technologies used in everyday life.
List of Referenes
- Shunsuke Koseki, Mitsuhiro Hayashibe, Dai Owaki. Identifying essential factors for energy-efficient walking control across a wide range of velocities in reflex-based musculoskeletal systems. PLOS Computational Biology, 2024; 20 (1): e1011771 DOI: 10.1371/journal.pcbi.1011771
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