Power assist and clinical application that could predict and control viscoelasticity for rehabilitation of stroke
| Project/Area Number |
16K01572
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Multi-year Fund |
|---|
| Section | 一般 |
|---|
| Research Field |
Rehabilitation science/Welfare engineering
|
|---|
| Research Institution | Tokyo Polytechnic University |
|---|
Principal Investigator |
Shin Duk 東京工芸大学, 工学部, 准教授 (00431982)
|
|---|
| Research Collaborator |
YANAGISAWA takuhumi
|
|---|
| Project Period (FY) |
2016-04-01 – 2019-03-31
|
| Project Status |
Completed (Fiscal Year 2018)
|
| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2018: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2017: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2016: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
|
|---|
| Keywords | リハビリテーション / ブレイン・マシン・インタ ーフェース / ECoG / パワーアシスト / 人口筋肉 / 空気圧ゴム / リニアモータ / ブレイン・マシン・インターフェース / デコーディング / インタラクション / 福祉・介護用ロボット / 粘弾性制御 |
|---|
| Outline of Final Research Achievements |
We predicted kinematic and kinetic information simultaneously from electrocorticogram (ECoG). The subject performed the reaching movement between two points passing through the designated via-point with three types of plastic bottles in weight. We could reproduce kinetic information (EMG signals) and kinematic information (trajectory) from ECoG using the proposed method. Since the patient’s ECG was recorded during only one week for their treatments and there were also very few cases involved the primary motor area, we used the data of monkey ECoG. We estimated the joint angle based on the same method. The 4 DOF robot arm was successfully controlled using the estimated joint angle. We also succeeded in controlling the artificial arm made with a 3D printer using steady-state visual evoked potentials.
|
| Academic Significance and Societal Importance of the Research Achievements |
障がい者がパワーアシストや電動義手を装着して異なる重さを持つ様々な物体とインタラクションするためには物体の重さを予測し, 適切な力学情報と運動情報を生体信号から推定しなければならない．本研究では提案手法により皮質脳波から筋電信号（力学情報）や関節角度（運動情報）を同時に推定することができ, 予測した筋電信号や関節角度を制御に入力することでロボットアームの制御に成功した．この成果に基づいて精密な制御が可能な多関節電動義手や高齢者向きのパワーアシスト装置や脳卒中のリハビリなど臨床へ応用が期待される．
|
Report
(4 results)
Research Products
(13 results)
-
-
-
-
-
-
-
[Presentation] Brain network analysis of hand motor execution and imagination based on Granger causality2019
Author(s)
Jiaxin Zhang, Rui Xu, Abdelkader Nasreddine Belkacem, Duk Shin, Kun Wang, Zhongpeng Wang, Bin Hao, Lu Yu, Zhifeng Qiao, Changming Wang, Chao Chen
Organizer
IMBioC2019
Related Report
Int'l Joint Research
-
-
[Presentation] Classification of EEG Multiple Imagination Tasks Based on Independent Component Analysis and Relevant Vector Machines2019
Author(s)
Shanting Zhang, Rui Xu, Abdelkader Nasreddine Belkacem, Duk Shin, Kun Wang, Zhongpeng Wang, Bin Hao, Lu Yu, Zhifeng Qiao, Changming Wang, Chao Chen
Organizer
IMBioC2019
Related Report
Int'l Joint Research
-
-
-
-
