| Project/Area Number |
16K12993
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| Research Category |
Grant-in-Aid for Challenging Exploratory Research
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| Allocation Type | Multi-year Fund |
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| Research Field |
Sports science
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| Research Institution | Yokohama National University |
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Principal Investigator |
SHIMA Keisuke 横浜国立大学, 大学院工学研究院, 准教授 (50649754)
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| Co-Investigator(Kenkyū-buntansha) |
島谷 康司 県立広島大学, 保健福祉学部(三原キャンパス), 教授 (00433384)
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| Research Collaborator |
SHINOHARA Minoru
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| Project Period (FY) |
2016-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥3,640,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥840,000)
Fiscal Year 2018: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2017: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2016: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Keywords | 機能的電気刺激 / 運動学習 / リハビリテーション / ニューラルネット / 振動刺激 / パターン認識 / 筋音図 / パターン識別 |
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| Outline of Final Research Achievements |
Extensive and focused physiotherapy is needed to help individuals with disabilities, such as hemiplegics, achieve natural physical movement involving the simultaneous use of various muscles. Here the authors outline a new approach to such work involving the use of FES (functional electrical stimulation) /vibrotactile stimulations and MMG (mechanomyogram) signals to help people with hemiplegia resulting from spinal injuries or cerebrovascular accidents (CVAs) achieve such muscle contraction and to enable related evaluation. The study showed that exponential functions can be utilized to clarify the correlation between the current used for stimulation and the joint angles of the forearm. It was found that electrical/vibrotactile stimulus and MMG signals can be used to transfer muscle contraction information between individuals for supporting of motor skill training in the rehabilitation and sports.
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| Academic Significance and Societal Importance of the Research Achievements |
高度な運動技術が求められるスポーツや自然な運動を訓練する関節運動リハビリテーションなどにおいては，適切かつ効果的な関節運動の学習・訓練が非常に重要である．複雑な身体運動は複数筋の随意的な協調収縮によって実現されるため，多数の筋を適切かつタイミングよく協調制御しなければ自然で効率的な運動を実現できない．我々が考案した技術は，スポーツなどの運動学習における複数筋の収縮状態を正確に評価し，運動意図に対応した適切な筋協調収縮状態と収縮のタイミング，力の強弱などを伝達・再現・増幅して効果的な訓練が可能である．これにより様々な運動学習に適用することが期待される．
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