Complementary multimodal biosiginal measurement and analysis for prosthetic hand control

2021 Fiscal Year Final Research Report

• Project/Area Number: 19K12877
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 90150:Medical assistive technology-related
• Research Institution: The University of Electro-Communications
• Principal Investigator: Jiang Yinlai 電気通信大学, 脳・医工学研究センター, 准教授 (70508340)
• Co-Investigator(Kenkyū-buntansha): 横井 浩史 電気通信大学, 大学院情報理工学研究科, 教授 (90271634)
• Project Period (FY): 2019-04-01 – 2022-03-31
• Keywords: 筋電義手 / 表面筋電図 / Force Myography

Outline of Final Research Achievements

Surface electromyography (sEMG) has been widely investigated as a biological signal from which motion intentions can be recognized to control prosthetic hands. The availability and quality of sEMG can limit the usability and intuitiveness of advanced prosthetic hands that can restore most necessary hand movements. This study introduces force myography (FMG) as a supplementary signal and develops a hybrid sensor to measure sEMG and FMG signals simultaneously. Furthermore, a layer-fusion convolutional neural network (CNN) was proposed to analyze the sEMG and FMG signals. The recognition results of hand motion showed a significantly improved classification accuracy (CA) of the hybrid sEMG and FMG with respect to individual modality due to the rubustness of FMG. The FMG-assisted sEMG sensing approach can effectively offer great potential in the clinical application of sophisticated prosthetic hands without increasing burden to the user.

Free Research Field

ヒューマンインターフェース

Academic Significance and Societal Importance of the Research Achievements

学術的意義：本研究は、筋活動の機能的信号であるsEMGと組織的信号であるFMGを異種信号として組み合わせることで、筋活動に含まれた動作意図をより多く、より正確的に読み取ることができるようになった。異種の生体信号を非侵襲的に読み取り、総合的に解析する方法、サイボーグの要素技術となり得る。
社会的意義：異種のsEMGとFMG信号を単体のセンサで同じ場所で計測できるため、信号の可用性が高く、計測系もシンプルなままであるので、高機能義手の使用性を改善し、使用範囲を飛躍的に広げることにつながると期待できる。