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

Satomi Shirasaki, Kenji Kanai, Jiro Katto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Consumer Electronics, ICCE 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728197661
DOIs
Publication statusPublished - 2021 Jan 10
Event2021 IEEE International Conference on Consumer Electronics, ICCE 2021 - Las Vegas, United States
Duration: 2021 Jan 102021 Jan 12

Publication series

NameDigest of Technical Papers - IEEE International Conference on Consumer Electronics
Volume2021-January
ISSN (Print)0747-668X

Conference

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

Keywords

  • IoT
  • LF/HF estimation
  • RRI estimation
  • deep learning

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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