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Learning and recognition with neural network of heart beats sensed by WBAN for stress estimate for rehabilitation
http://hdl.handle.net/10131/00012743
http://hdl.handle.net/10131/0001274387db7675-a318-4447-9dca-f47508e4cb8e
名前 / ファイル | ライセンス | アクション |
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BODYNETS2018_Yukihiro Kinjo.pdf (270.5 kB)
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Item type | 会議発表論文 / Conference Paper(1) | |||||
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公開日 | 2019-09-05 | |||||
タイトル | ||||||
タイトル | Learning and recognition with neural network of heart beats sensed by WBAN for stress estimate for rehabilitation | |||||
言語 | ||||||
言語 | eng | |||||
キーワード | ||||||
主題 | rehabilitation | |||||
キーワード | ||||||
主題 | WBAN | |||||
キーワード | ||||||
主題 | emotion recognition | |||||
キーワード | ||||||
主題 | neural network | |||||
キーワード | ||||||
主題 | heartbeat | |||||
資源タイプ | ||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_5794 | |||||
資源タイプ | conference paper | |||||
著者 |
Kinjo, Yukihiro
× Kinjo, Yukihiro× Sakuma, Yoshitomo× Kohno, Ryuji |
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著者所属 | ||||||
Yokohama National University | ||||||
抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | In rehabilitation, approach according to personality is important. So we estimate patients' emotion by neural network(NN) for their R-R interval(RRI) in heart rate data from Wireless Body Area Network(WBAN). However, machine learning processing is complexity and sending heart rate data to super computer like Watson for machine learning processing causes network delay. In this research, we propose how to reduce computational complexity to enable to calculate by limited processing power. Specifically, we aim to reduce it to use NN with preprocessing by wavelet transform and extraction of coefficient of variance of RRI(CVRR). Preprocessing extract a part of characteristic before processing of NN and computational complexity by NN processing reduce. |
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書誌情報 |
IEICE Technical Report 巻 118, 号 24, p. 101-104, 発行日 2018-05-25 |
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権利 | ||||||
権利情報 | copyright©2019 IEICE | |||||
著者版フラグ | ||||||
出版タイプ | VoR | |||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||
出版者 | ||||||
出版者 | IEICE |