| Project/Area Number |
21K18105
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 90150:Medical assistive technology-related
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| Research Institution | Kyushu University |
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Principal Investigator |
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| Project Period (FY) |
2021-04-01 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2022: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2021: ¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
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| Keywords | Wearable Robotics / Rehabilitation / Esoskeleton / HDEMG / Gabor filters / Motion Intention / HD EMG / High Density EMG |
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| Outline of Research at the Start |
Wearable robots need to be controlled according to the human motion intention. Existing technologies cannot predict the intentions intuitively. HDEMG contains information related to motor unit activations. This study investigates a new paradigm using HD EMG to estimate human motion intention.
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| Outline of Final Research Achievements |
In wearable robotic systems like exoskeletons and prostheses, capturing the motion intention of their wearer is crucial for intuitive control to supplement or support the intended motion. In this study, high-density electromyography (HDEMG) was utilized as a biological signal to extract human motion intention, focusing on a prosthetic hand user. The human hand possesses a higher number of degrees of freedom (DOFs). However, conventional muscle signal measurement and analysis techniques are inadequate to provide sufficient information related to this higher number of DOFs. This study demonstrated that variations in spatial activations of human muscles, measured with HDEMG can offer adequate information to estimate motion intention for higher DOFs with higher accuracy in real-time. Initially, the spatial information of the human muscles was mapped into heatmaps. Later, corresponding information related to the spatial changes in the heatmaps over time was used to estimate intended motion.
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| Academic Significance and Societal Importance of the Research Achievements |
This study will provide insights into how enough information related to multi DOFs of human moiton can be extracted from HDEMG for motion intention estimation, considering the spatial variations of the muscle activations. This will help to improve the quality of life of wearable robotic users.
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