Spinal Information Processing and its Application to Motor Learning Support

We designed motor learning support for acquiring motor skills involving neural mechanisms. We should be able to acquire neural information by analyzing whole-body muscle data, because the nervous system controls the musculoskeletal system and lengths and forces information is fed back to the nervous system. Motor information is calculated by mapping motioncapture data on to a musculoskeletal human model. Neural information represents the set of motor information on the muscles innervated by the arbitrary spinal cord segment. Neural information processing is proposed which calculates correlation among the neural information. We demonstrate the effectiveness of our proposal by experimental results of “kesagiri”.