| Project/Area Number |
17K01360
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Biomedical engineering/Biomaterial science and engineering
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| Research Institution | Okayama University |
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Principal Investigator |
Oka Hisao 岡山大学, ヘルスシステム統合科学研究科, 特命教授 (80116441)
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| Co-Investigator(Kenkyū-buntansha) |
福原 真一 川崎医療福祉大学, 医療技術学部, 講師 (70745497)
渡辺 彰吾 岡山大学, 保健学研究科, 准教授 (20548341)
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| Project Period (FY) |
2017-04-01 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2019: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2018: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2017: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
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| Keywords | 筋音図 / 筋電図 / MMG / EMG / 筋収縮 / 変位筋音図 |
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| Outline of Final Research Achievements |
In this study, the authors developed a MMG / EMG hybrid sensor system that can be used practically in order to measure mechanomyogram (MMG) signals in addition to electromyogram signals (EMG) simultaneously. The research goal was to establish the effectiveness of the muscular contraction performance index and spread it widely in Japan and overseas.
Including the developed integrated / separate type hybrid transducer, the authors developed the measurement software for Windows / Android version with Bluetooth wireless. In addition, the authors proposed a new performance index by applying Parseval's theorem from MMG and EMG signals, and applied it to exercise evaluation of wheel-chair pedaling and of standing-up electric assisted tricycles, and confirmed its effectiveness.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究で開発された「ＭＭＧ／ＥＭＧハイブリッドセンサシステム」は、リハビリテーション分野を含む臨床医学や、スポーツ科学、さらに介護福祉分野などで実使用されており、多くのデータが蓄積されてきた。本センサは、既に国内企業が販売（http://www.erd.co.jp/pg116.html）しており、自由に購入して使用できる環境が整いつつある。新たに手術室における筋収縮機能の評価、薬剤の効果判定等、新規分野への適用も試みられ、当研究グループもサポートしているが、今後の応用・展開が期待できる。
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