Advertisement

Development of a Sleep Monitoring System by Using a Depth Sensor: A Pilot Study

  • Jangwoon ParkEmail author
  • Jungyoon Kim
  • Jaehyun Park
  • Alaa Sheta
  • Christina Murphey
  • Dugan Um
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 957)

Abstract

Sleep is an essential part of health and longevity persons. As people grow older, the quality of their sleep becomes vital. Poor sleep quality can make negative physiological, psychological, and social impacts on the elderly population, causing a range of health problems including coronary heart disease, depression, anxiety, and loneliness. Early detection, proper diagnosis, and treatments for sleep disorders can be achieved by identifying sleep patterns through long-term sleep monitoring. Although many studies developed sleep monitoring systems by using non-invasive measures such as body temperature, pressure, or body movement signal, research is still limited to detect sleep position changes by using a depth camera. The present study is intended (1) to identify concerns on the existing sleep monitoring system based on the literature review and (2) propose to developing a non-invasive sleep monitoring system using an infrared depth camera. For the literature review, various journal/conference papers have been reviewed to understand the characteristics, tools, and algorithms of the existing sleep monitoring systems. For the system development and validation, we collected data for the sleep positions from two subjects (35 years old man and 84 years old women) during the four-hour sleep. Kinect II depth sensor was used for data collection. We found that the averaged depth data is useful measure to notify the participants’ positional changes during the sleep.

Keywords

Sleep quality Sleep movement Sleep pattern Kinect II depth sensor 

Notes

Acknowledgements

This project was supported by a TCRF Program Development grant from Division of Research, Commercialization and Outreach at Texas A&M University-Corpus Christi.

References

  1. 1.
    Ortman, J.M., Velkoff, V.A., Hogan, H.: An aging nation: the older population in the United States, pp. 25–1140. United States Census Bureau, Economics and Statistics Administration, US Department of Commerce (2014)Google Scholar
  2. 2.
    Seneviratne, S., Hu, Y., Nguyen, T., Lan, G., Khalifa, S., Thilakarathna, K., Seneviratne, A.: A survey of wearable devices and challenges. IEEE Commun. Surv. Tutor. 19(4), 2573–2620 (2017)CrossRefGoogle Scholar
  3. 3.
    Rahman, T., Adams, A.T., Ravichandran, R.V., Zhang, M., Patel, S.N., Kientz, J.A., Choudhury, T.: DoppleSleep: a contactless unobtrusive sleep sensing system using short-range Doppler radar. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 39–50. ACM, September 2015Google Scholar
  4. 4.
    Liu, X., Cao, J., Tang, S., Wen, J., Guo, P.: Contactless respiration monitoring via off-the-shelf WiFi devices. IEEE Trans. Mob. Comput. 15(10), 2466–2479 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Jangwoon Park
    • 1
    Email author
  • Jungyoon Kim
    • 2
  • Jaehyun Park
    • 3
  • Alaa Sheta
    • 4
  • Christina Murphey
    • 5
  • Dugan Um
    • 1
  1. 1.Department of EngineeringTexas A&M University-Corpus ChristiCorpus ChristiUSA
  2. 2.Department of Computer ScienceKent State UniversityKentUSA
  3. 3.Department of Industrial and Management EngineeringIncheon National UniversityIncheonSouth Korea
  4. 4.Department of Computing SciencesTexas A&M University-Corpus ChristiCorpus ChristiUSA
  5. 5.College of Nursing and Health SciencesTexas A&M University-Corpus ChristiCorpus ChristiUSA

Personalised recommendations