research-article

Recognition of sleep patterns using a bed pressure mat

Authors:

Vangelis Metsis,

Georgios Galatas,

Alexandros Papangelis,

Dimitrios Kosmopoulos,

Fillia MakedonAuthors Info & Claims

PETRA '11: Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments

Article No.: 9, Pages 1 - 4

https://doi.org/10.1145/2141622.2141633

Published: 25 May 2011 Publication History

Abstract

The monitoring of sleep patterns is of major importance for various reason 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 bed pressure mat to assess and report sleep patterns. To evaluate our system we used real data collected in Heracleia Lab's assistive living apartment. Our method 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.

References

[1]

D. Berndt and J. Clifford. Using dynamic time warping to find patterns in time series. In AAAI-94 workshop on knowledge discovery in databases, pages 229--248, 1994.

[2]

T. Cover and P. Hart. Nearest neighbor pattern classification. Information Theory, IEEE Transactions on, 13(1):21--27, 2002.

Digital Library

[3]

G. H. Dunteman. Principal components analysis. Sage Publications, Inc., 1989.

[4]

A. E. Flores, J. E. Flores, H. Deshpande, J. A. Picazo, X. Xie, R. Franken, H. C. Heller, D. A. Grahn, and B. F. O'Hara. Pattern recognition of sleep in rodents using piezoelectric signals generated by gross body movements. IEEE transactions on bio-medical engineering, 54(2):225, 2007.

[5]

D. Foley, S. Ancoli-Israel, P. Britz, and J. Walsh. Sleep disturbances and chronic disease in older adults: Results of the 2003 national sleep foundation sleep in america survey. Journal of Psychosomatic Research, 56(5):497--502, 2004.

[6]

B. H. Jansen and W.-K. Cheng. Classification of sleep patterns by means of markov modeling and correspondence analysis. Pattern Analysis and Machine Intelligence, IEEE Transactions on, PAMI-9(5):707--710, 1987.

Digital Library

[7]

W. Liao and C. Yang. Video-based activity and movement pattern analysis in overnight sleep studies. Proceedings, 3(Conf 19):1774--1777, 2008.

[8]

J. V. Marcos, R. Hornero, D. Alvarez, F. del Campo, and C. Zamarron. Assessment of four statistical pattern recognition techniques to assist in obstructive sleep apnoea diagnosis from nocturnal oximetry. Medical engineering & physics., 31(8):971, 2009.

[9]

H. Ni, B. Abdulrazak, D. Zhang, S. Wu, Z. Yu, X. Zhou, S. Wang, and I. conference; 7th. Towards non-intrusive sleep pattern recognition in elder assistive environment. 2010.

[10]

A. A. of Sleep Medicine. International classification of sleep disorders, revised: Diagnostic and coding manual. 2001.

[11]

J. M. S. Pearce. Restless leg syndrome. Eur Neurol, 53(4):206--207, 2005.

[12]

L. Rabiner. A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2):257--286, Feb. 1989.

[13]

Sabri Boughorbel, Fons Bruekers, and Jeroen Breebaart. Baby-Posture Classification from Pressure-Sensor Data. ICPR, pages 556--559, 2010.

Digital Library

[14]

M. Vandeputte and A. de Weerd. Sleep disorders and depressive feelings: a global survey with the beck depression scale. Sleep Medicine, 4(4):343--345, 2003.

[15]

S. Young, G. Evermann, D. Kershaw, G. Moore, J. Odell, D. Ollason, D. Povey, V. Valtchev, and P. Woodland. The HTK Book. 2002.

Cited By

Ocagli HLanera CBorghini CKhan NCasamento AGregori D(2024)In-Bed Monitoring: A Systematic Review of the Evaluation of In-Bed Movements Through Bed SensorsInformatics10.3390/informatics1104007611:4(76)Online publication date: 22-Oct-2024
https://doi.org/10.3390/informatics11040076
Lee TLee HLee JKim J(2023)Ensemble Approach to Combining Episode Prediction Models Using Sequential Circadian Rhythm Sensor Data from Mental Health PatientsSensors10.3390/s2320854423:20(8544)Online publication date: 18-Oct-2023
https://doi.org/10.3390/s23208544
Stern LRoshan Fekr A(2023)In-Bed Posture Classification Using Deep Neural NetworkSensors10.3390/s2305243023:5(2430)Online publication date: 22-Feb-2023
https://doi.org/10.3390/s23052430
Show More Cited By

Index Terms

Recognition of sleep patterns using a bed pressure mat
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Image and video acquisition
        Motion capture
  2. Computer graphics
    1. Animation
      1. Motion capture
      2. Motion processing

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Published In

cover image ACM Other conferences

PETRA '11: Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments

May 2011

401 pages

ISBN:9781450307727

DOI:10.1145/2141622

Program Chairs:
Margrit Betke
Boston University
,
Ilias Maglogiannis
University of Central Greece, Greece
,
Grammati Pantziou
TEI of Athens (Department of Informatics), Greece

Copyright © 2011 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]

Sponsors

NSF: National Science Foundation
Foundation of the Hellenic World
ICS-FORTH: Institute of Computer Science, Foundation for Research and Technology - Hellas
U of Tex at Arlington: U of Tex at Arlington
UCG: University of Central Greece
Didaskaleio Konstantinos Karatheodoris, University of the Aegean
Fulbrigh, Greece: Fulbright Foundation, Greece
Ionian: Ionian University, GREECE

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 May 2011

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National Science Foundation

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PETRA '11

Sponsor:

NSF
ICS-FORTH
U of Tex at Arlington
UCG
Fulbrigh, Greece
Ionian

PETRA '11: The 4th International Conference on PErvasive Technologies Related to Assistive Environments

May 25 - 27, 2011

Crete, Heraklion, Greece

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Cited By

Ocagli HLanera CBorghini CKhan NCasamento AGregori D(2024)In-Bed Monitoring: A Systematic Review of the Evaluation of In-Bed Movements Through Bed SensorsInformatics10.3390/informatics1104007611:4(76)Online publication date: 22-Oct-2024
https://doi.org/10.3390/informatics11040076
Lee TLee HLee JKim J(2023)Ensemble Approach to Combining Episode Prediction Models Using Sequential Circadian Rhythm Sensor Data from Mental Health PatientsSensors10.3390/s2320854423:20(8544)Online publication date: 18-Oct-2023
https://doi.org/10.3390/s23208544
Stern LRoshan Fekr A(2023)In-Bed Posture Classification Using Deep Neural NetworkSensors10.3390/s2305243023:5(2430)Online publication date: 22-Feb-2023
https://doi.org/10.3390/s23052430
Stern LRoshan Fekr A(2023)Recent Advances and Applications of Textile Technology in Patient MonitoringHealthcare10.3390/healthcare1123306611:23(3066)Online publication date: 29-Nov-2023
https://doi.org/10.3390/healthcare11233066
Wan QZhao HLi JXu P(2023)Human Sleeping Posture Recognition Based on Sleeping Pressure ImageIEEE Sensors Journal10.1109/JSEN.2022.322529023:4(4069-4077)Online publication date: 15-Feb-2023
https://doi.org/10.1109/JSEN.2022.3225290
Fonseca LRibeiro FMetrôlho J(2022)Lying-People Pressure-Map Datasets: A Systematic ReviewData10.3390/data80100128:1(12)Online publication date: 30-Dec-2022
https://doi.org/10.3390/data8010012
Zhou BSuh SRey VAltamirano CLukowicz P(2022)Quali-MatProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35346106:2(1-45)Online publication date: 7-Jul-2022
https://dl.acm.org/doi/10.1145/3534610
Hu QTang XTang W(2021)A Real-Time Patient-Specific Sleeping Posture Recognition System Using Pressure Sensitive Conductive Sheet and Transfer LearningIEEE Sensors Journal10.1109/JSEN.2020.304341621:5(6869-6879)Online publication date: 1-Mar-2021
https://doi.org/10.1109/JSEN.2020.3043416
Matar GLina JKaddoum G(2020)Artificial Neural Network for in-Bed Posture Classification Using Bed-Sheet Pressure SensorsIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2019.289907024:1(101-110)Online publication date: Jan-2020
https://doi.org/10.1109/JBHI.2019.2899070
Liang ZPloderer BLiu WNagata YBailey JKulik LLi Y(2016)SleepExplorerPersonal and Ubiquitous Computing10.1007/s00779-016-0960-620:6(985-1000)Online publication date: 1-Nov-2016
https://dl.acm.org/doi/10.1007/s00779-016-0960-6
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