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Monitoring breathing activity and sleep patterns using multimodal non-invasive technologies

Authors:

Michalis Papakostas,

James Staud,

Fillia Makedon,

Vangelis MetsisAuthors Info & Claims

PETRA '15: Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments

Article No.: 78, Pages 1 - 4

https://doi.org/10.1145/2769493.2769585

Published: 01 July 2015 Publication History

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Abstract

The monitoring of sleeping behavioral patterns is of major importance for various reasons such as the detection and treatment of sleep disorders, the assessment of the effect of different medical conditions or medications on the sleep quality, and the assessment of mortality risks associated with sleeping patterns in adults and children. Sleep monitoring by itself is a difficult problem due to both privacy and technical considerations.

The proposed system uses a combination of non-invasive sensors to assess and report sleep patterns and breathing activity: a contact-based pressure mattress and a non-contact 2D image acquisition device. To evaluate our system, we used real data collected in Heracleia Lab's assistive living apartment. Our system uses Machine Learning and Computer Vision techniques to automatically analyze the collected data, recognize sleep patterns and track the breathing behavior. It is non-invasive, as it does not disrupt the user's usual sleeping behavior and it can be used both at the clinic and at home with minimal cost. Going one step beyond, we developed a mobile application for visualizing the analyzed data and monitor the patient's sleep status remotely.

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Cited By

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Liu JZhang JXu RPearce ANi WHedley M(2020)Gaussian Mixture Model based Convolutional Sparse Coding for Radar Heartbeat Detection2020 14th International Conference on Signal Processing and Communication Systems (ICSPCS)10.1109/ICSPCS50536.2020.9310063(1-6)Online publication date: 14-Dec-2020
https://doi.org/10.1109/ICSPCS50536.2020.9310063
Mohammadi SKouchaki SSanei SDijk DHilton AWells K(2019)Tensor Factorisation and Transfer Learning for Sleep Pose Detection2019 27th European Signal Processing Conference (EUSIPCO)10.23919/EUSIPCO.2019.8902979(1-5)Online publication date: Sep-2019
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Hu MZhai GLi DFan YDuan HZhu WYang X(2018)Combination of near-infrared and thermal imaging techniques for the remote and simultaneous measurements of breathing and heart rates under sleep situationPLOS ONE10.1371/journal.pone.019046613:1(e0190466)Online publication date: 5-Jan-2018
https://doi.org/10.1371/journal.pone.0190466
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Monitoring breathing activity and sleep patterns using multimodal non-invasive technologies

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Published In

PETRA '15: Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments

July 2015

526 pages

ISBN:9781450334525

DOI:10.1145/2769493

Conference Chair:
Fillia Makedon
University of Texas Arlington
,
Program Chairs:
Gian-Luca Mariottini,
Oliver Korn,
Illias Maglogiannis,
Vangelis Metsis

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

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Published: 01 July 2015

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NSF
University of Texas at Austin
Univ. of Piraeus
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Ionian

PETRA '15: 8th PErvasive Technologies Related to Assistive Environments

July 1 - 3, 2015

Corfu, Greece

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Liu JZhang JXu RPearce ANi WHedley M(2020)Gaussian Mixture Model based Convolutional Sparse Coding for Radar Heartbeat Detection2020 14th International Conference on Signal Processing and Communication Systems (ICSPCS)10.1109/ICSPCS50536.2020.9310063(1-6)Online publication date: 14-Dec-2020
https://doi.org/10.1109/ICSPCS50536.2020.9310063
Mohammadi SKouchaki SSanei SDijk DHilton AWells K(2019)Tensor Factorisation and Transfer Learning for Sleep Pose Detection2019 27th European Signal Processing Conference (EUSIPCO)10.23919/EUSIPCO.2019.8902979(1-5)Online publication date: Sep-2019
https://doi.org/10.23919/EUSIPCO.2019.8902979
Hu MZhai GLi DFan YDuan HZhu WYang X(2018)Combination of near-infrared and thermal imaging techniques for the remote and simultaneous measurements of breathing and heart rates under sleep situationPLOS ONE10.1371/journal.pone.019046613:1(e0190466)Online publication date: 5-Jan-2018
https://doi.org/10.1371/journal.pone.0190466
Basavaraju PVarde A(2017)Supervised Learning Techniques in Mobile Device Apps for AndroidsACM SIGKDD Explorations Newsletter10.1145/3068777.306878218:2(18-29)Online publication date: 22-Mar-2017
https://dl.acm.org/doi/10.1145/3068777.3068782
Menghan Hu Guangtao Zhai Duo Li Yezhao Fan Huiyu Duan Wenhan Zhu Xiaokang Yang (2017)Dual-mode imaging system for non-contact heart rate estimation during night2017 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)10.1109/ICMEW.2017.8026243(97-102)Online publication date: Jul-2017
https://doi.org/10.1109/ICMEW.2017.8026243
Lioulemes APapakostas MGieser SToutountzi TAbujelala MGupta SCollander CMcmurrough CMakedon F(2016)A Survey of Sensing Modalities for Human Activity, Behavior, and Physiological MonitoringProceedings of the 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments10.1145/2910674.2910711(1-8)Online publication date: 29-Jun-2016
https://dl.acm.org/doi/10.1145/2910674.2910711

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Non-invasive Techniques for Monitoring Different Aspects of Sleep: A Comprehensive Review

A cost-effective and non-invasive system for sleep and vital signs monitoring using passive RFID tags

Non-invasive analysis of sleep patterns via multimodal sensor input

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