2021 Fiscal Year Final Research Report
Sensing the Signs of Fall Occurrence in the Elderly
| Project/Area Number |
19K22735
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| Research Category |
Grant-in-Aid for Challenging Research (Exploratory)
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| Allocation Type | Multi-year Fund |
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| Review Section |
Medium-sized Section 58:Society medicine, nursing, and related fields
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| Research Institution | Tohoku University |
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Principal Investigator |
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| Project Period (FY) |
2019-06-28 – 2022-03-31
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| Keywords | 転倒予測 / 慣性センサ / ニューラルネットワーク / クラスタリング / 歩隔 / 睡眠 |
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| Outline of Final Research Achievements |
Based on the idea of detecting signs of falls from gait changes, a method for detecting gait change using a neural network was developed. The results showing the feasibility of classifying gait pattern and classifying factors that cause gait change by using unsupervised learning were also obtained. Then, in order to estimate values of indices of factors that cause gait changes related to falls, methods to detect and evaluate abnormal foot movements in the early stance phase during walking, to estimate automatically stride length and walking speed, to detect automatically gait event timings, and to estimate the body center of mass position for evaluating balance during walking were developed using inertial sensors. Furthermore, a correlation between total sleep time and the feeling of walking instability in healthy subjects was found, which suggested the possibility of using total sleep time in detecting signs of falls.
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| Free Research Field |
生体医工学
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| Academic Significance and Societal Importance of the Research Achievements |
転倒に関する多くの研究は，転倒が発生した結果の検出や，転倒しやすいタイプかどうかの判定にとどまっており，工学的技術を利用した転倒発生の兆候の検出の研究はほとんど行われていない．本研究は，看護師らの経験に基づく転倒の兆候の検出を工学的技術により実現することを目指して，運動情報と内面的状態に関係する生体情報や睡眠情報の利用可能性を検討し，簡便な慣性センサでの歩行計測で，転倒に関わる未知の歩容変化検出とその要因を分類する方法の実現可能性，睡眠情報の利用可能性を示す結果を得た．これらの結果は，転倒発生の兆候を検出する技術につながる点で学術的意義があり，転倒予防の実現の観点で社会的意義がある．
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