Prediction of functional independence measure (FIM) using machine learning and wearable data of stroke inpatients in a convalescent rehabilitation ward
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- OGASAWARA Takayuki
- NTT Corporation
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- TANAKA Kentaro
- NTT Corporation
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- MUKAINO Masahiko
- Fujita Health University
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- OTAKA Yohei
- Fujita Health University
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- YAMAGUCHI Masumi
- NTT Corporation
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- SAITOH Eiichi
- Fujita Health University
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- TSUKADA Shingo
- NTT Corporation
Bibliographic Information
- Other Title
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- ウェアラブルデータと機械学習を用いた回復期リハビリ病棟脳卒中患者の機能指標(FIM)予測
Description
<p>This study aimed to estimate the score of motor FIM (Functional independence measure) of stroke inpatients, which is used as a clinical indicator in rehabilitation medicine, using wearable data recorded in a convalescent rehabilitation ward. We recorded the electrocardiogram and acceleration data of 192 stroke inpatients over a day. To estimate the score of motor FIM, we trained neural network using the wearable data and basic information of inpatients such as weight, height, sexuality and age, and then, performed five-fold cross validation. In the result, the coefficient of determination between estimated FIM by neural network and ground truth scored by therapists showed 0.73 and it was significant (p < 0.001), suggesting the possibility of estimation of motor function of stroke inpatients using activity record obtained with wearable devices.</p>
Journal
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- Proceedings of the Annual Conference of JSAI
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Proceedings of the Annual Conference of JSAI JSAI2021 (0), 3F2GS10j05-3F2GS10j05, 2021
The Japanese Society for Artificial Intelligence
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Details 詳細情報について
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- CRID
- 1390006895525097216
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- NII Article ID
- 130008051864
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- Text Lang
- ja
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- Data Source
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- JaLC
- CiNii Articles
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- Abstract License Flag
- Disallowed