Advertisement

Personal Recommendation System for Improving Sleep Quality

  • Patrick Datko
  • Wilhelm Daniel Scherz
  • Oana Ramona Velicu
  • Ralf SeepoldEmail author
  • Natividad Martínez Madrid
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 56)

Abstract

Sleep is an important aspect in life of every human being. The average sleep duration for an adult is approximately 7 h per day. Sleep is necessary to regenerate physical and psychological state of a human. A bad sleep quality has a major impact on the health status and can lead to different diseases. In this paper an approach will be presented, which uses a long-term monitoring of vital data gathered by a body sensor during the day and the night supported by mobile application connected to an analyzing system, to estimate sleep quality of its user as well as give recommendations to improve it in real-time. Actimetry and historical data will be used to improve the individual recommendations, based on common techniques used in the area of machine learning and big data analysis.

Keywords

Obstructive Sleep Apnea Sleep Quality Sleep Duration Sleep Stage Body Sensor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Balakrishnan, G., Burli, D., Behbehani, K., Burk, J., Lucas, E.: Comparison of a sleep quality index between normal and obstructive sleep apnea patients. In: 27th Annual International Conference of the Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005, pp. 1154–1157 (2005)Google Scholar
  2. 2.
    Bsoul, M., Minn, H., Nourani, M., Gupta, G., Tamil, L.: Real-time sleep quality assessment using single-lead ecg and multi-stage svm classifier. In: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1178–1181 (2010)Google Scholar
  3. 3.
    Cheng, S.P., Mei, H.: A personalized sleep quality assessment mechanism based on sleep pattern analysis. In: 2012 Third International Conference on Innovations in Bio-Inspired Computing and Applications (IBICA), pp. 133–138 (Sept 2012)Google Scholar
  4. 4.
    Dafna, E., Tarasiuk, A., Zigel, Y.: Sleep-quality assessment from full night audio recordings of sleep apnea patients. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3660–3663 (2012)Google Scholar
  5. 5.
    Miwa, H., Sasahara, S.i., Matsui, T.: Roll-over detection and sleep quality measurement using a wearable sensor. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007. EMBS 2007, pp. 1507–1510 (2007)Google Scholar
  6. 6.
    Peng, Y.T., Lin, C.Y., Sun, M.T., Landis, C.: Multimodality sensor system for long-term sleep quality monitoring. IEEE Trans. Biomed. Circuits Syst. 1(3), 217–227 (2007)CrossRefGoogle Scholar
  7. 7.
    Wakuda, Y., Hasegawa, Y., Fukuda, T., Noda, A., Arai, F., Kawaguchi, M.: Estimation of sleep cycle and quality based on nonlinear analysis of heart rate variability. In: Proceedings of the 2004 International Symposium on Micro-Nanomechatronics and Human Science, 2004 and the Fourth Symposium Micro-Nanomechatronics for Information-Based Society, pp. 181–186 (2004)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Patrick Datko
    • 1
  • Wilhelm Daniel Scherz
    • 1
  • Oana Ramona Velicu
    • 1
  • Ralf Seepold
    • 1
    Email author
  • Natividad Martínez Madrid
    • 2
  1. 1.HTWG KonstanzUbiquitous Computing LabKonstanzGermany
  2. 2.Reutlingen University, Internet of Things LabReutlingenGermany

Personalised recommendations