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Voluntary Movements

開催日 2014/9/13
時間 17:10 - 18:10
会場 Room H(304)
Chairperson(s) 小池 康晴 / Yasuharu Koike (東京工業大学 / Tokyo Institute of Technology, Japan)
筒井 健一郎 / Ken-ichiro Tsutsui (東北大学大学院生命科学研究科 脳情報処理分野 / Division of Systems Neuroscience, Graduate School of Life Sciences, Tohoku University, Japan)

Neural synergy: The Future of Motor Function Development and Recovery

  • O3-H-5-1
  • Fady Sk Alnajjar:1 shingo shimoda:1 
  • 1:Intelligent Behavior Control Unit, BSI, RIKEN, Nagoya, Japan 

Understanding our brain mechanisms, including cognition, emotion, sensation, behavioral adaptation, etc., are essential to gain fundamental knowledge of our neural system. This understanding will not only assist the development and the recovery of our neural system when needed, but also to achieve robust bio-inspired intelligent systems, such as: neuroprosthetics, neurorobotics, etc. In this particular study, we are targeting neuromuscular strategies behind behavioral adaptation and recovery in healthy participants and stroke survivals. Relying on the concept of muscle synergy that defines by low-dimensional signals the synchronization level of several muscles acting for complex motor behavior, we first developed various physiological markers to quantify the motor skill in healthy participants. We then evaluated the markers to classify the motor disabilities of cortical stroke survivors. Three novel markers were yet designed and verified through tasks involving automatic body responses and voluntary actions: 1) the synergy stability index (SSI), which measures the similarities between muscle usage in repeated behaviors and therefore the stability of the neural command. 2) The synergy coordination index (SCI), which measures the overall size of the synergy space required to carry out a movement and therefore the level of coordination between muscles. 3) The synergy dimensionality index (SDI), which measures the number of neural commands sufficient to control the muscles to produce the desired behavior. Currently, we are expanding the project by designing a neuro-feedback model based on sensory and muscle synergies. The model driven by an intelligent robot will aim to assist the development of both skill acquisition and therapeutic approaches, in the hope of making conventional training and rehabilitative systems effortless and more effective. Functional neuroimaging study are planned to be used to track neural level changes along the training period to validate our model

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