| アイテムタイプ |
共通アイテムタイプ(1) |
| 公開日 |
2025-02-05 |
| タイトル |
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|
タイトル |
Forecasting Sit-to-Stand Transitions in Wheelchair Patients: a Textile Pressure Sensor-based Approach |
|
言語 |
en |
| 著者 |
Tazin, Tahia
Victorino, John Noel
井上, 創造
Enokibori, Yu
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| 著作権関連情報 |
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|
権利情報 |
Copyright (c) 2024 Author |
| 著作権関連情報 |
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|
権利情報Resource |
https://creativecommons.org/licenses/by/4.0/deed.ja |
|
権利情報 |
This article is licensed under a Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/deed.ja |
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
This paper introduces the identification between safe standup and risky standup activity using a sit-to-stand transition prediction system from 2D pressure sensor data to mitigate the occurrence of unexpected falls. For wheelchair users, the sit-to-stand transition is a vital daily activity requiring considerable physical effort and balance control. Elderly people, especially those with dementia, may experience significant adverse effects if they cannot perform sit-to-stand correctly, which can result in falls and serious injuries. In this regard, an e-textile pressure sensor-based wheelchair opens up possibilities to reduce unexpected falls by tracking behavioral activities, such as sit-to-stand transition. In the laboratory environment, we collect 20 subjects' pressure sensor data from these modified wheelchairs to forecast sit-to-stand activity (e.g.,trying to standup and assistive standup) and other daily activities (e.g., sitting, exercising, and eating). For predicting these activities, we investigated various machine learning techniques, such as ResNet-50, Long short-term memory (LSTM), XGBoost (XGB), Random Forest (RnF), K-Nearest Neighbor (KNN), Support vector machine (SVM). In this study, we also evaluated the performance of various statistical feature sets for 2D pressure sensor data. Overall, the proposed system can potentially improve the safety and quality of life of wheelchair patients by preventing falls and reducing the risk of serious injuries. |
|
言語 |
en |
| 備考 |
|
|
内容記述タイプ |
Other |
|
内容記述 |
5th International Conference on Activity and Behavior Computing, ABC2023, September 7 - 9, 2023, Kaiserslautern, Germany (Hybrid) |
|
言語 |
en |
| 書誌情報 |
en : International Journal of Activity and Behavior Computing
巻 2024,
号 1,
p. 1-20,
発行日 2024-05-09
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| 出版社 |
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出版者 |
九州工業大学ケアXDXセンター |
|
言語 |
ja |
| 言語 |
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|
言語 |
eng |
| 資源タイプ |
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|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| DOI |
|
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|
識別子タイプ |
DOI |
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|
関連識別子 |
https://doi.org/10.60401/ijabc.7 |
| ISSN |
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|
収録物識別子タイプ |
EISSN |
|
収録物識別子 |
2759-2871 |
| 会議記述 |
|
|
|
会議名 |
5th International Conference on Activity and Behavior Computing, ABC2023 |
|
|
言語 |
en |
|
回次 |
5 |
|
|
開始年 |
2023 |
|
|
開始月 |
09 |
|
|
開始日 |
07 |
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終了年 |
2023 |
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|
終了月 |
09 |
|
|
終了日 |
09 |
|
開催国 |
DEU |
| 研究者情報 |
|
|
URL |
https://hyokadb02.jimu.kyutech.ac.jp/html/140_ja.html |
| 論文ID（連携） |
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|
値 |
10422669 |
| 連携ID |
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|
値 |
12740 |
10422669.pdf (3.4 MB)
