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Real-time robust estimation of breathing rate from PPG using commercial-grade smart devices: demo abstract

Authors:

Vivek Chandel,

Jayeeta Saha,

Chirayata Bhattacharyya,

Avik GhoseAuthors Info & Claims

SenSys '20: Proceedings of the 18th Conference on Embedded Networked Sensor Systems

Pages 633 - 634

https://doi.org/10.1145/3384419.3430458

Published: 16 November 2020 Publication History

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Abstract

In this work, we present a solution for an accurate and real-time monitoring of breathing rate (BR) from photoplethysmogram (PPG) signal on both smartphone and smartwatch. Respiration induces multiple modulations in a PPG signal which are difficult to extract from low-quality PPG signal collected using consumer devices. We present an effective method of validating the breathing signal data which is evaluated and compared on an open dataset. The solution is also implemented as a smartphone and smartwatch app to provide an on-device real-time BR, and evaluated on multiple subjects. For the demonstration, we shall show breathing rate and breathing pattern both on smartphone and smartwatch, which can be visualized in real-time on a dashboard.

References

[1]

Peter H Charlton, Drew A Birrenkott, Timothy Bonnici, Marco AF Pimentel, Alistair EW Johnson, Jordi Alastruey, Lionel Tarassenko, Peter J Watkinson, Richard Beale, and David A Clifton. 2017. Breathing rate estimation from the electrocardiogram and photoplethysmogram: A review. IEEE reviews in biomedical engineering 11 (2017), 2--20.

Google Scholar

[2]

Sungjun Kwon, Hyunseok Kim, and Kwang Suk Park. 2012. Validation of heart rate extraction using video imaging on a built-in camera system of a smartphone. In 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2174--2177.

Crossref

Google Scholar

[3]

Dean J Miller, John V Capodilupo, Michele Lastella, Charli Sargent, Gregory D Roach, Victoria H Lee, and Emily R Capodilupo. 2020. Analyzing changes in respiratory rate to predict the risk of COVID-19 infection. medRxiv (2020).

Google Scholar

[4]

Samsung Newsroom. 2020 (accessed September 25, 2020. Samsung Announces Blood Pressure Monitoring Application for Galaxy Watch Devices. https://news.samsung.com/global/samsung-announces-blood-pressure-monitoring-application-for-galaxy-watch-devices

Google Scholar

[5]

Dung Phan, Lee Yee Siong, Pubudu N Pathirana, and Aruna Seneviratne. 2015. Smartwatch: Performance evaluation for long-term heart rate monitoring. In 2015 International symposium on bioelectronics and bioinformatics (ISBB). IEEE, 144--147.

Crossref

Google Scholar

[6]

Marco AF Pimentel, Alistair EW Johnson, Peter H Charlton, Drew Birrenkott, Peter J Watkinson, Lionel Tarassenko, and David A Clifton. 2016. Toward a robust estimation of respiratory rate from pulse oximeters. IEEE Transactions on Biomedical Engineering 64, 8 (2016), 1914--1923.

Crossref

Google Scholar

Cited By

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Vincent KWang LJing NLiu W(2024)AcHand: Detecting Respiratory Rate of Operator Via Smartphone MicrophoneIEEE Sensors Journal10.1109/JSEN.2024.337225524:8(12946-12956)Online publication date: 15-Apr-2024
https://doi.org/10.1109/JSEN.2024.3372255
Sullivan JUl Alam M(2022)PhysioGait: Context-Aware Physiological Context Modeling for Person Re-identification Attack on Wearable Sensing2022 18th International Conference on Mobility, Sensing and Networking (MSN)10.1109/MSN57253.2022.00062(331-338)Online publication date: Dec-2022
https://doi.org/10.1109/MSN57253.2022.00062
Selvakumar KVinodh Kumar ESailesh MVarun MAllan ABiswajit NNamrata PUpasana S(2022)Realtime PPG based respiration rate estimation for remote health monitoring applicationsBiomedical Signal Processing and Control10.1016/j.bspc.2022.10374677(103746)Online publication date: Aug-2022
https://doi.org/10.1016/j.bspc.2022.103746
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Real-time robust estimation of breathing rate from PPG using commercial-grade smart devices: demo abstract

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Published In

SenSys '20: Proceedings of the 18th Conference on Embedded Networked Sensor Systems

November 2020

852 pages

ISBN:9781450375900

DOI:10.1145/3384419

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 November 2020

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SenSys '20

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SenSys '20: The 18th ACM Conference on Embedded Networked Sensor Systems

November 16 - 19, 2020

Virtual Event, Japan

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Overall Acceptance Rate 174 of 867 submissions, 20%

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Cited By

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Vincent KWang LJing NLiu W(2024)AcHand: Detecting Respiratory Rate of Operator Via Smartphone MicrophoneIEEE Sensors Journal10.1109/JSEN.2024.337225524:8(12946-12956)Online publication date: 15-Apr-2024
https://doi.org/10.1109/JSEN.2024.3372255
Sullivan JUl Alam M(2022)PhysioGait: Context-Aware Physiological Context Modeling for Person Re-identification Attack on Wearable Sensing2022 18th International Conference on Mobility, Sensing and Networking (MSN)10.1109/MSN57253.2022.00062(331-338)Online publication date: Dec-2022
https://doi.org/10.1109/MSN57253.2022.00062
Selvakumar KVinodh Kumar ESailesh MVarun MAllan ABiswajit NNamrata PUpasana S(2022)Realtime PPG based respiration rate estimation for remote health monitoring applicationsBiomedical Signal Processing and Control10.1016/j.bspc.2022.10374677(103746)Online publication date: Aug-2022
https://doi.org/10.1016/j.bspc.2022.103746
Bhavsar KSinghal SChandel VSamal AKhandelwal SAhmed NGhose A(2021)Digital Biomarkers: Using Smartwatch Data For Clinically Relevant Outcomes2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)10.1109/PerComWorkshops51409.2021.9431000(630-635)Online publication date: 22-Mar-2021
https://doi.org/10.1109/PerComWorkshops51409.2021.9431000
Haddad SBoukhayma ACaizzone A(2021)PPG-Based Respiratory Rate Monitoring Using Hybrid Vote-Aggregate Fusion Technique2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)10.1109/EMBC46164.2021.9630685(1605-1608)Online publication date: 1-Nov-2021
https://doi.org/10.1109/EMBC46164.2021.9630685

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Extraction of Respiration from PPG Signals Using Hilbert Vibration Decomposition

Robust CNN-based Respiration Rate Estimation for Smartwatch PPG and IMU

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