demonstration

HeartSense: smart phones to estimate blood pressure from photoplethysmography

Authors:

Rohan Banerjee,

Anirban Dutta Choudhury,

Aniruddha Sinha,

Aishwarya VisvanathanAuthors Info & Claims

SenSys '14: Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems

Pages 322 - 323

https://doi.org/10.1145/2668332.2668378

Published: 03 November 2014 Publication History

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Abstract

In this paper we propose to demonstrate a smart phone application, that estimates human blood pressure (BP) values from photoplethysmography (PPG) signal using Windkessel model. PPG signal is extracted from a video sequence of a user's index fingertip, acquired using smart phone camera. A set of time domain PPG features are used to estimate different lumped parameters of Windkessel model to simulate arterial BP. Under most of the cases, the application estimates systolic and diastolic BP values, within a range of ±10% of clinical measurement.

References

[1]

A. Pal, A. Sinha, A. Choudhury, T. Chattopadhyay, and A. Visvanathan, "A robust heart rate detection using smart-phone video," in Mobile-Health Workshop in MobiHoc, 2013.

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[2]

A. D. Choudhury, A. Visvanathan, R. Banerjee, A. Sinha, A. Pal, C. Bhaumik, and A. Kumar, "Heartsense: estimating blood pressure and ecg from photoplethysmograph using smart phones," in Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems, p. 88, ACM, 2013.

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[3]

R. Banerjee, A. Sinha, A. Dutta Choudhury, and A. Visvanathan, "PhotoECG: photoplethysmography to estimate ECG parameters," in ICASSP2014 - Bio Imaging and Signal Processing (ICASSP2014 - BISP), (Florence, Italy), pp. 4437--4441, May 2014.

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Cited By

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Visco VIzzo CMancusi CRispoli ATedeschi MVirtuoso NGiano AGioia RMelfi ASerio BRusciano MDi Pietro PBramanti AGalasso GD’Angelo GCarrizzo AVecchione CCiccarelli M(2023)Artificial Intelligence in Hypertension Management: An Ace up Your SleeveJournal of Cardiovascular Development and Disease10.3390/jcdd1002007410:2(74)Online publication date: 9-Feb-2023
https://doi.org/10.3390/jcdd10020074
Xuan YBarry CAntipa NWang E(2023)A calibration method for smartphone camera photophlethysmographyFrontiers in Digital Health10.3389/fdgth.2023.13010195Online publication date: 24-Nov-2023
https://doi.org/10.3389/fdgth.2023.1301019
Aggarwal NAhmed MBasu SCurtin JEvans BMatheny MNundy SSendak MShachar CShah RThadaney-Israni S(2020)Advancing Artificial Intelligence in Health Settings Outside the Hospital and ClinicNAM Perspectives10.31478/202011fOnline publication date: 30-Nov-2020
https://doi.org/10.31478/202011f
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HeartSense: smart phones to estimate blood pressure from photoplethysmography

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Information

Published In

SenSys '14: Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems

November 2014

380 pages

ISBN:9781450331432

DOI:10.1145/2668332

General Chair:
Ákos Lédecz
Vanderbilt University
,
Program Chairs:
Prabal Dutta
University of Michigan
,
Chenyang Lu
Washington Univ. in St. Louis

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 November 2014

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Demonstration

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SenSys '14

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SenSys '14: The 12th ACM Conference on Embedded Network Sensor Systems

November 3 - 6, 2014

Tennessee, Memphis

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Overall Acceptance Rate 174 of 867 submissions, 20%

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Cited By

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Visco VIzzo CMancusi CRispoli ATedeschi MVirtuoso NGiano AGioia RMelfi ASerio BRusciano MDi Pietro PBramanti AGalasso GD’Angelo GCarrizzo AVecchione CCiccarelli M(2023)Artificial Intelligence in Hypertension Management: An Ace up Your SleeveJournal of Cardiovascular Development and Disease10.3390/jcdd1002007410:2(74)Online publication date: 9-Feb-2023
https://doi.org/10.3390/jcdd10020074
Xuan YBarry CAntipa NWang E(2023)A calibration method for smartphone camera photophlethysmographyFrontiers in Digital Health10.3389/fdgth.2023.13010195Online publication date: 24-Nov-2023
https://doi.org/10.3389/fdgth.2023.1301019
Aggarwal NAhmed MBasu SCurtin JEvans BMatheny MNundy SSendak MShachar CShah RThadaney-Israni S(2020)Advancing Artificial Intelligence in Health Settings Outside the Hospital and ClinicNAM Perspectives10.31478/202011fOnline publication date: 30-Nov-2020
https://doi.org/10.31478/202011f
Elgendi MFletcher RLiang YHoward NLovell NAbbott DLim KWard R(2019)The use of photoplethysmography for assessing hypertensionnpj Digital Medicine10.1038/s41746-019-0136-72:1Online publication date: 26-Jun-2019
https://doi.org/10.1038/s41746-019-0136-7
Alam SDewangan KSinharay AGhose A(2016)A generic sensing and processing framework for smartphones for heterogeneous sensing2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)10.1109/PERCOMW.2016.7457143(1-6)Online publication date: Mar-2016
https://doi.org/10.1109/PERCOMW.2016.7457143
Alam SDewangan KSinharay AGhose A(2016)Mobile sensing framework for task partitioning between cloud and edge device for improved performance2016 IEEE Symposium on Computers and Communication (ISCC)10.1109/ISCC.2016.7543769(379-384)Online publication date: Jun-2016
https://doi.org/10.1109/ISCC.2016.7543769
Ahmed NBanerjee RGhose ASinharay AAshok ALiu JBanerjee S(2015)Feasibility Analysis for Estimation of Blood Pressure and Heart Rate using A Smart Eye WearProceedings of the 2015 workshop on Wearable Systems and Applications10.1145/2753509.2753511(9-14)Online publication date: 18-May-2015
https://dl.acm.org/doi/10.1145/2753509.2753511
Misra ABanerjee RChoudhury ASinha APal A(2015)Novel peak detection to estimate HRV using smartphone audio2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)10.1109/BSN.2015.7299378(1-6)Online publication date: Jun-2015
https://doi.org/10.1109/BSN.2015.7299378

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