TY - GEN
T1 - Deep Learning-based R-R Interval Estimation by Using Smartphone Sensors during Exercise
AU - Shirasaki, Satomi
AU - Kanai, Kenji
AU - Katto, Jiro
N1 - Funding Information:
The research leading to these results has been supported by the EU-JAPAN initiative by the EC Horizon 2020 Work Programme (2018-2020) Grant Agreement No.814918 and Ministry of Internal Affairs and Communications “Strategic Information and Communications R&D Promotion Programme (SCOPE)” Grant no. JPJ000595, “Federating IoT and cloud infrastructures to provide scalable and interoperable Smart Cities applications, by introducing novel IoT virtualization technologies (Fed4IoT)”.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/1/10
Y1 - 2021/1/10
N2 - 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.
AB - 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.
KW - IoT
KW - LF/HF estimation
KW - RRI estimation
KW - deep learning
UR - http://www.scopus.com/inward/record.url?scp=85106031136&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85106031136&partnerID=8YFLogxK
U2 - 10.1109/ICCE50685.2021.9427634
DO - 10.1109/ICCE50685.2021.9427634
M3 - Conference contribution
AN - SCOPUS:85106031136
T3 - Digest of Technical Papers - IEEE International Conference on Consumer Electronics
BT - 2021 IEEE International Conference on Consumer Electronics, ICCE 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Consumer Electronics, ICCE 2021
Y2 - 10 January 2021 through 12 January 2021
ER -