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'Biologically accurate' robot legs walk like an Egyptian

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Vid US boffins have come up with a pair of robotic legs that they reckon are the first to walk in a biologically accurate (if somewhat jerky) manner.

The researchers want to try to mimic the actual process of walking, particularly the bit where people don't actually have to think to do it, so they can figure out how babies learn to get around and possibly help spinal-cord-injury patients to regain the ability.

So for their robot trousers, the boffins put in simplified versions of the neural and musculoskeletal architecture and sensory feedback pathways that humans have.

The key to people walking is the central pattern generator, a neural network in the spinal cord that generates rhythmic muscle signals. The CPG works by picking up info from different parts of the body that are responding to the environment and using them to produce and control the rhythm.

The robotic legs use the simplest version of a CPG, a half-centre, which consists of just two neurones firing alternatively to set the rhythm, as well as sensors feeding into the half-centre. For example, load sensors use the force in the limb to tell when the leg is being pressed down for a step.

"Interestingly, we were able to produce a walking gait, without balance, which mimicked human walking with only a simple half-centre controlling the hips and a set of reflex responses controlling the lower limb," study co-author Dr Theresa Klein said in a canned statement.

The boffins now think that that might be how babies start out, with a simple half-centre, which would explain why they are able to show a walking pattern on a treadmill before they learn to walk. Over time, the baby then expends the network for more complex walking patterns.

"This underlying network may also form the core of the CPG and may explain how people with spinal cord injuries can regain walking ability if properly stimulated in the months after the injury," Klein added.

The University of Arizona researchers' study has been published in IOP's Journal of Neural Engineering. ®

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