Fundamental study on the resolution of kinematic redundancy for creation of FES stimulus data by using Artificial Neural Network

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  • 人工神経回路を用いたFES刺激データ生成のための運動学的冗長性解消の基礎的検討

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Functional Electrical Stimulation (FES) is a technique for restoration of lost motor functions of paralyzed muscles. The authors have been studying a creation method of stimulus data by using Artificial Neural Network (ANN) which mimics the musculoskeletal system of paralyzed patients. "Direct inverse modeling" that we adopt as a learning method of ANN cannot be applied to a redundant object such as the musculoskeletal system. Hence a constraint is used for the resolution of kinematic redundancy. In this paper, we study the performance of the constraints with a musculoskeletal simulator that includes synergistic and/or antagonistic muscles.

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