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Biomechanics Model: Humans’ Efficient Walking Inspires Robotics Advances

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Walking, a complex task that we often take for granted, involves the coordination of our musculoskeletal system, guided by our nervous system. A team from Tohoku University Graduate School of Engineering has made strides in replicating this process in robotics, creating a model that mimics human-like variable speed walking.

Their study, published in PLoS Computational Biology, used a reflex control method that mirrors the human nervous system. This achievement is a significant step forward in understanding human movement and opens up new possibilities for innovative robotic technologies.

The team, including Associate Professor Dai Owaki, Shunsuke Koseki, and Professor Mitsuhiro Hayashibe, developed an innovative algorithm that goes beyond the conventional least squares method. This algorithm helped them create a neural circuit model optimized for energy efficiency across different walking speeds.

By analyzing these neural circuits, especially those controlling the leg swing phase, they discovered key aspects of energy-saving walking strategies. This understanding enhances our knowledge of the complex neural network mechanisms that make human gait so efficient.

Owaki believes that their findings will form the basis for future technological advancements. The successful emulation of variable-speed walking in a musculoskeletal model, combined with sophisticated neural circuitry, is a major breakthrough in the integration of neuroscience, biomechanics, and robotics. This could revolutionize the design and development of high-performance bipedal robots, advanced prosthetic limbs, and cutting-edge powered exoskeletons.

Such advancements could improve mobility for individuals with disabilities and further the development of robotic technologies used in everyday life.

In the future, Owaki and his team plan to refine the reflex control framework to recreate a wider range of human walking speeds and movements. They also aim to use the insights and algorithms from their study to create more adaptive and energy-efficient prosthetics, powered suits, and bipedal robots.

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