Skip to main content
Springer Nature Link
Log in

Non-invasive analysis of sleep patterns via multimodal sensor input

  • Original Article
  • Published:
Personal and Ubiquitous Computing Aims and scope Submit manuscript

Abstract

The monitoring of sleep 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: a contact-based pressure mattress and a non-contact 3D image acquisition device, which can complement each other. To evaluate our system, we used real data collected in Heracleia Lab’s assistive living apartment. Our system uses Machine Learning techniques to automatically analyze the collected data and recognize sleep patterns. 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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. http://www.pressuremapping.com/.

References

  1. American Academy of Sleep Medicine (2001) International classification of sleep disorders, revised: diagnostic and coding manual. American Academy of Sleep Medicine, Illinois

  2. Boughorbel S, Bruekers F, Breebaart J (2010) Baby-posture classification from pressure-sensor data. In: ICPR, pp 556–559

  3. Charles J, Everingham M (2011) Learning shape models for monocular human pose estimation from the microsoft xbox kinect. In: IEEE international conference on computer vision workshops (ICCV workshops), pp 1202–1208

  4. Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21–27

    Article  MATH  Google Scholar 

  5. Cristianini N, Shawe-Taylor J (2000) An introduction to support Vector Machines: and other kernel-based learning methods. Cambridge University Press, Cambridge

    Book  Google Scholar 

  6. Dunteman GH (1989) Principal components analysis. Sage Publications Inc., Thousand Oaks

    Google Scholar 

  7. Flores AE, Flores JE, Deshpande H, Picazo JA, Xie X, Franken R, Heller HC, Grahn DA, O’Hara BF (2007) Pattern recognition of sleep in rodents using piezoelectric signals generated by gross body movements. IEEE Trans Bio-med Eng 54(2):225

    Article  Google Scholar 

  8. Foley D, Ancoli-Israel S, Britz P, Walsh J (2004) Sleep disturbances and chronic disease in older adults: results of the 2003 national sleep foundation sleep in america survey. J Psychosom Res 56(5):497–502. doi:10.1016/j.jpsychores.2004.02.010. http://www.sciencedirect.com/science/article/B6T8V-4CG6MR1-5/2/e6f128d71f0fcc69c302ec316c194a62

  9. Jansen BH, Cheng WK (1987) Classification of sleep patterns by means of markov modeling and correspondence analysis. IEEE Trans Pattern Anal Mach Intell PAMI-9(5):707–710. doi:10.1109/TPAMI.1987.4767968

    Google Scholar 

  10. Jin Z, Lou Z, Yang J, Sun Q (2007) Face detection using template matching and skin-color information. Neurocomputing 70(4–6):794–800

    Article  Google Scholar 

  11. Kinect-xbox.com. http://www.xbox.com/en-US/kinect. Accessed 26 Oct 2012

  12. Li W, Zhang Z, Liu Z (2010) Action recognition based on a bag of 3d points. In: CVPR4HB10, pp 9–14

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

    Google Scholar 

  14. Marcos JV, Hornero R, Alvarez D, del Campo F, Zamarron C (2009) Assessment of four statistical pattern recognition techniques to assist in obstructive sleep apnoea diagnosis from nocturnal oximetry. Med Eng Phys 31(8):971

    Article  Google Scholar 

  15. Ni H, Abdulrazak B, Zhang D, Wu S, Yu Z, Zhou X, Wang S (2010) 7th Conference, I.: Towards non-intrusive sleep pattern recognition in elder assistive environment

  16. Rabiner L (1989) A tutorial on hidden markov models and selected applications in speech recognition. Proc IEEE 77(2):257–286. doi:10.1109/5.18626

  17. Sung J, Ponce C, Selman B, Saxena A (2012) Unstructured human activity detection from RGBD images. In: Proceedings of the International Conference on Robotics and Automation (ICRA). IEEE, pp 842–849

  18. Susan R, Sonia AI (2010) Sleep disorders in the elderly. Indian J Med Res 131:302–310

    Google Scholar 

  19. Vandeputte M, de Weerd A (2003) Sleep disorders and depressive feelings: a global survey with the beck depression scale. Sleep Med 4(4):343–345. doi:10.1016/S1389-9457(03)00059-5. http://www.sciencedirect.com/science/article/B6W6N-4949GT9-G/2/08444f7069f4981ee4e60c13d836b96f

Download references

Author information

Authors and Affiliations

  1. Heracleia Human-Centered Computing Lab, Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, USA

    Vangelis Metsis, Dimitrios Kosmopoulos & Fillia Makedon

  2. Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, USA

    Vassilis Athitsos

Authors
  1. Vangelis Metsis
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Dimitrios Kosmopoulos
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Vassilis Athitsos
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Fillia Makedon
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Vangelis Metsis.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Metsis, V., Kosmopoulos, D., Athitsos, V. et al. Non-invasive analysis of sleep patterns via multimodal sensor input. Pers Ubiquit Comput 18, 19–26 (2014). https://doi.org/10.1007/s00779-012-0623-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00779-012-0623-1

Keywords