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short-paper

Using mobile phones to simulate pulse oximeters: gait analysis predicts oxygen saturation

Authors:

Bruce R. SchatzAuthors Info & Claims

BCB '14: Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

Pages 331 - 340

https://doi.org/10.1145/2649387.2649403

Published: 20 September 2014 Publication History

Abstract

Widespread availability of mobile devices is leading revolution in health monitoring. Smartphones have demonstrated strong potential as ubiquitous monitors, but it is still unclear what vital signs can be monitored. We present a novel machine learning model to track oxygen saturation from phone sensors. Oxygen saturation is perhaps the major composite vital sign, widely used as a single measure of health status during medical procedures. We compute spatio-temporal gait parameters from phone sensor data, then use these to predict oxygen saturation with a trained model. We compare alternative approaches in each step of model training to discover optimal solutions. The validation shows training models on cohorts performs better than training a universal model. We ran unconstrained 6 minute walk tests on 15 senior pulmonary patients to predict oxygen saturation. The absolute error rate is about 1% for the cohort models and about 2% for the universal model. This can be compared to the 1% resolution of the pulse oximeter serving as standard and the 4% drop in values signifying desaturation. We also find that characteristic patterns in oxygen saturation as predicted correlate closely with the pulmonary function status. Thus smart phones have great potential to monitor desaturation in chronic patients.

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Cited By

Trifan AOliveira MOliveira J(2018)Passive sensing of health outcomes through smartphones: a systematic review of current solutions and possible limitations (Preprint)JMIR mHealth and uHealth10.2196/12649Online publication date: 30-Oct-2018
https://doi.org/10.2196/12649
Cheng QShang JJuen JHan JSchatz B(2016)Mining Discriminative Patterns to Predict Health Status for Cardiopulmonary PatientsProceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics10.1145/2975167.2975171(41-49)Online publication date: 2-Oct-2016
https://dl.acm.org/doi/10.1145/2975167.2975171

Index Terms

Using mobile phones to simulate pulse oximeters: gait analysis predicts oxygen saturation
1. Applied computing
  1. Life and medical sciences
    1. Consumer health
    2. Health informatics

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cover image ACM Conferences

BCB '14: Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

September 2014

851 pages

ISBN:9781450328944

DOI:10.1145/2649387

General Chairs:
Pierre Baldi
University of California, Irvine
,
Wei Wang
University of California, Los Angeles

Copyright © 2014 ACM.

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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Published: 20 September 2014

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Regional College of Medicine
U.S. Department of Agriculture

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BCB '14

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SIGBio

BCB '14: ACM-BCB '14

September 20 - 23, 2014

California, Newport Beach

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Cited By

Trifan AOliveira MOliveira J(2018)Passive sensing of health outcomes through smartphones: a systematic review of current solutions and possible limitations (Preprint)JMIR mHealth and uHealth10.2196/12649Online publication date: 30-Oct-2018
https://doi.org/10.2196/12649
Cheng QShang JJuen JHan JSchatz B(2016)Mining Discriminative Patterns to Predict Health Status for Cardiopulmonary PatientsProceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics10.1145/2975167.2975171(41-49)Online publication date: 2-Oct-2016
https://dl.acm.org/doi/10.1145/2975167.2975171

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