Deep Learning-based R-R Interval Estimation by Using Smartphone Sensors during Exercise

Satomi Shirasaki, Kenji Kanai, Jiro Katto

研究成果: Conference contribution

抄録

Recently, aging of the population and medical cost inflation are emerging as social issues to be solved. To address this problem, estimations of bioinformation based on Internet of Things (IoT) and machine learning get more attention to researchers. In this paper, in order to estimate R-R Interval (RRI) without using specialized and professional wearable devices, we propose a deep learning based RRI estimation method by using mainly smartphone sensors. For dataset, we collect ECG (for label), 3-axis acceleration, pressure, illuminance, GPS and temperature (for training data) under different exercise types (walking and running) by using a smartphone and smart clothing called hitoe. To construct a regression model, we adopt a dual stage attention-based RNN model. From the evaluation results, we confirm that the proposed method can estimate RRI and LF/HF with acceptable accuracy.

本文言語English
ホスト出版物のタイトル2021 IEEE International Conference on Consumer Electronics, ICCE 2021
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728197661
DOI
出版ステータスPublished - 2021 1 10
イベント2021 IEEE International Conference on Consumer Electronics, ICCE 2021 - Las Vegas, United States
継続期間: 2021 1 102021 1 12

出版物シリーズ

名前Digest of Technical Papers - IEEE International Conference on Consumer Electronics
2021-January
ISSN(印刷版)0747-668X

Conference

Conference2021 IEEE International Conference on Consumer Electronics, ICCE 2021
国/地域United States
CityLas Vegas
Period21/1/1021/1/12

ASJC Scopus subject areas

  • 産業および生産工学
  • 電子工学および電気工学

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