| Presentation | 2020-11-26 [Poster Presentation] Accuracy Evaluations of LSTM-based RRI Estimation Method by Using Smartphone Sensors During Exercise Satomi Shirasaki, Kenji Kanai, Jiro Katto, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Recently, because of aging of the population and increasing medical spending, it is required to shift from the conventional treatment centered medical care to preventive treatment. Therefore, demands for early diagnosis and handy monitoring in daily life is increasing. To address this fact, Internet of Things (IoT) and deep learning get more attention. In this paper, we propose an R-R Interval (RRI) estimation method based on deep learning using smartphone sensors to estimate the RRI without using special medical devices. To construct dataset, we collect ECG, 3-axis acceleration, pressure, illuminance, GPS, and temperature while walking and running by using a smart wear called hitoe and a smartphone. By using the dataset, we adopt a dual stage attention based RNN model to estimate RRI and evaluate the accuracy. The evaluation results conclude that the proposed method can estimate RRI and LF/HF with high accuracy. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | RRI estimation / LF/HF estimation / deep learning / IoT |
| Paper # | SeMI2020-29 |
| Date of Issue | 2020-11-19 (SeMI) |
| Conference Information | |
| Committee | SRW / SeMI / CNR |
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| Conference Date | 2020/11/26(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | IoT Workshop |
| Chair | Satoshi Denno(Okayama Univ.) / Susumu Ishihara(Shizuoka Univ.) / Kazunori Takashio(Keio Univ.) |
| Vice Chair | Keiichi Mizutani(Kyoto Univ.) / Kentaro Saito(Tokyo Inst. of Tech.) / Hanako Noda(Anritsu) / Kazuya Monden(Hitachi) / Koji Yamamoto(Kyoto Univ.) / Masayuki Kanbara(NAIST) / Yoshihiko Murakawa(Fujitsu Labs.) |
| Secretary | Keiichi Mizutani(NTT) / Kentaro Saito(NIigata Univ.) / Hanako Noda(Kyoto Univ.) / Kazuya Monden(Osaka Univ.) / Koji Yamamoto(Hitachi) / Masayuki Kanbara(Waseda Univ.) / Yoshihiko Murakawa(Shibaura Inst. of Tech.) |
| Assistant | Masaaki Fuse(Anritsu) / Akihito Noda(Nanzan Univ.) / Yuki Katsumata(NTT DOCOMO) / Yu Nakayama(Tokyo Univ. of Agri. and Tech.) / Akira Uchiyama(Osaka Univ.) / Yuka Kobayashi(Toshiba) / Masanori Yokoyama(ＮＴＴ) |
| Paper Information | |
| Registration To | Technical Committee on Short Range Wireless Communications / Technical Committee on Sensor Network and Mobile Intelligence / Technical Committee on Cloud Network Robotics |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | [Poster Presentation] Accuracy Evaluations of LSTM-based RRI Estimation Method by Using Smartphone Sensors During Exercise |
| Sub Title (in English) | |
| Keyword(1) | RRI estimation |
| Keyword(2) | LF/HF estimation |
| Keyword(3) | deep learning |
| Keyword(4) | IoT |
| 1st Author's Name | Satomi Shirasaki |
| 1st Author's Affiliation | Waseda University(Waseda Univ.) |
| 2nd Author's Name | Kenji Kanai |
| 2nd Author's Affiliation | Waseda University(Waseda Univ.) |
| 3rd Author's Name | Jiro Katto |
| 3rd Author's Affiliation | Waseda University(Waseda Univ.) |
| Date | 2020-11-26 |
| Paper # | SeMI2020-29 |
| Volume (vol) | vol.120 |
| Number (no) | SeMI-261 |
| Page | pp.pp.57-58(SeMI), |
| #Pages | 2 |
| Date of Issue | 2020-11-19 (SeMI) |