Research on a new evaluation index for selecting walking support devices that reflects the remaining capacity of limbs

2023 Fiscal Year Final Research Report

• Project/Area Number: 20K20269
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 90150:Medical assistive technology-related
• Research Institution: Tokyo Denki University
• Principal Investigator: Inoue Jun 東京電機大学, 工学部, 教授 (20609284)
• Project Period (FY): 2020-04-01 – 2024-03-31
• Keywords: リハビリテーション / 支援機器 / 福祉機器 / 残存能力 / 片麻痺

Outline of Final Research Achievements

In this study, we continued to develop a walker for hemiplegic patients to train them to walk with a cane, targeting patients with a certain amount of residual limb capacity. By predicting falls, we have made it possible to predict movement speed 0.5 gait cycles ahead from the acceleration of each part of the body, enabling use even by patients with more severe symptoms and lower residual function. Furthermore, there are large individual differences in the degree of disability, such as limb paralysis, and as a result, there are areas where measuring devices cannot be attached, such as when braces are used. In contrast, we were able to consider sensor placement to compensate for sensor deficiencies in conjunction with residual function, allowing predictions of movement speed to be made with high accuracy even when acceleration sensors at specific locations cannot be used.

Free Research Field

福祉工学

Academic Significance and Societal Importance of the Research Achievements

本研究では、片麻痺患者向けの歩行器の開発を継続し、転倒を予測することで、より重症の患者にも対応できるようにしました。具体的には、身体各部の加速度から0.5歩行周期先の移動速度を予測し、装具を使用する部位でも高精度な予測を可能にするセンサ配置を検討しました。さらに、腱への振動刺激による運動錯覚を利用して歩行運動に介入できることを示し、動作時の刺激周波数が静止時より高いことを明らかにしました。また、靴内の圧力や剪断力を計測する新しいセンサを開発し、振動と圧力、振動と剪断力の関係を機械学習で解析し、推定モデルを構築しました。この技術は医療・福祉用だけでなく、競技用靴や衣服への応用も期待できます。