Skip to main content

Advertisement

SpringerLink
Log in

Mining Based Time-Series Sleeping Pattern Analysis for Life Big-Data

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Humans require rest in order to recover from physical and psychological fatigue caused by daily routines, and either medication or sleep is needed to achieve this. If there is a lack of sleep, many health issues can occur, such as depression, anxiety, obsession, dementia, deteriorated immunity, and lowered physical ability. In modern society, a variety of sleep disorders including insomnia, hypnolepsy, sleep apnea, and anxietas tibiarum, occur depending on the environment or occupations. As such, sleep significantly influences the quality of life, and it is a fundamental factor of healthcare. Thus, to increase the quality of sleep, research has been conducted on sleep disorder treatment in various fields including humanities, social studies, science, and engineering. In addition, data collection and pattern analysis of sleep have been researched and developed in detail. Although diverse sleep management products using IT convergence technology have been released, they only provide simple numbers or graphs of the personal sleep data measured. Therefore, this study proposes a mining-based time-series sleep pattern analysis of life big data. The proposed method collects and pre-processes sleep logs obtained continuously from life big data over time. As a result of pre-processing, sleep indexes comprise total sleep time, light sleep, deep sleep, awakening time, and bed time. External and internal factors that influence a sleep pattern are designed as context information, and then are normalized. In addition, context information is processed as a context index to create sleep data transactions. Based on the context index, similar users are identified based on similar context information. An expectation maximization algorithm is used to calculate maximum a posteriori through repetition of the expectation step, and a maximization step is applied for clustering. In sleep data transactions of a user set of a cluster, the AprioriAll algorithm of data mining is applied to create a sequential pattern of sleep in order to find meaningful associations, patterns, and trends. A pattern fitting a sleep stage is analyzed in order to improve sleep habits and treat sleep disorders. To improve REM sleep and ensure that a sufficient number of sleep hours are obtained, a mining-based time-series pattern is offered as feedback to each user. In this way, it is possible to improve the quality of sleep and life of a user.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

References

  1. Jung, H., & Chung, K. (2016). Life style improvement mobile service for high risk chronic disease based on PHR platform. Cluster Computing, 19(2), 967–977.

    Article  Google Scholar 

  2. Rho, M. J., Jang, K. S., Chung, K., & Choi, I. Y. (2015). Comparison of knowledge, attitudes, and trust for the use of personal health information in clinical research. Multimedia Tools and Applications, 74(7), 2391–2404.

    Article  Google Scholar 

  3. Chung, K., Kim, J. C., & Park, R. C. (2016). Knowledge-based health service considering user convenience using hybrid Wi-Fi P2P. Information Technology and Management, 17(1), 67–80.

    Article  Google Scholar 

  4. Chung, K., & Park, R. C. (2016). PHR open platform based smart health service using distributed object group framework. Cluster Computing, 19(1), 505–517.

    Article  Google Scholar 

  5. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (DSM-5 ® ). Washington, D. C.: American Psychiatric Publishing.

    Book  Google Scholar 

  6. Chung, K. Y., Kim, J. H., Kang, U. G., Rim, K. W., & Lee, J. H. (2008). Discovery of behavior sequence pattern using mining in smart home. Korea Contents Association, 8(9), 19–26.

    Article  Google Scholar 

  7. Chang, J. H. (2011). Mining interesting sequential pattern with a time-interval constraint for efficient analyzing a web-click stream. Korea Industrial Information Systems Research, 16(2), 19–29.

    Article  Google Scholar 

  8. Pyo, S., Kim, E., & Kim, M. (2009). A personalized automatic TV program scheduler using sequential pattern mining. Broadcast Engineering, 14(5), 625–637.

    Article  Google Scholar 

  9. Edéll-Gustafsson, U. M., Kritz, E. I., & Bogren, I. K. (2002). Self-reported sleep quality, strain and health in relation to perceived working conditions in females. Caring Sciences, 16(2), 179–187.

    Article  Google Scholar 

  10. Baek, Y., Yoo, J., Lee, S. W., & Jin, H. J. (2013). Domestic trends of research and patent for sleep disorder. Korea Contents Association, 13(6), 309–317.

    Article  Google Scholar 

  11. Nandakumar, R., Gollakota, S., & Watson, N. (2015). Contactless sleep apnea detection on smartphones. In Proceedings of the 13th annual international conference on mobile systems, applications, and services (pp. 45–57).

  12. Lee, B. M., & Hwang, H. J. (2015). Virtual sleep sensor with PSQI for sleep therapy service. Korea Multimedia Society, 18(12), 1538–1546.

    Article  Google Scholar 

  13. Hello Sense. https://hello.is/. Accessed 28 Mar 2018.

  14. Fitbit. https://www.fitbit.com/. Accessed 1 Apr 2018.

  15. Beddit. https://www.beddit.com/. Accessed 29 Mar 2018.

  16. National Sleep Foundation. https://sleepfoundation.org/. Accessed 2 Apr 2018.

  17. Agrawal, R., & Srikant, R. (1995). Mining sequential patterns. In Proceedings of the international conference on data engineering, IEEE CS (pp. 3–14).

  18. Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: A k-means clustering algorithm. Royal Statistical Society. Series C (Applied Statistics), 28(1), 100–108.

    MATH  Google Scholar 

  19. Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society Series B (methodological), 39(1), 1–38.

    Article  MathSciNet  MATH  Google Scholar 

  20. Kim, J. C., & Chung, K. (2018). Mining health-risk factors using PHR similarity in a hybrid P2P network. Peer-to-Peer Networking and Applications, 11(6), 1278–1287.

    Article  Google Scholar 

  21. Masseglia, F., Teisseire, M., & Poncelet, P. (2005). Sequential pattern mining. In Encyclopedia of data warehousing and mining, IGI Global (pp. 1028–1032).

  22. Oh, S. Y., & Chung, K. (2018). Performance evaluation of silence-feature normalization model using cepstrum features of noise signals. Wireless Personal Communications, 98(4), 3287–3297.

    Article  Google Scholar 

  23. Bengio, Y., & Grandvalet, Y. (2004). No unbiased estimator of the variance of k-fold cross-validation. Journal of Machine Learning Research, 5, 1089–1105.

    MathSciNet  MATH  Google Scholar 

  24. Kim, J. C., & Chung, K. (2017). Depression index service using knowledge based crowdsourcing in smart health. Wireless Personal Communication, 93(1), 255–268.

    Article  Google Scholar 

  25. Yoo, H., & Chung, K. (2017). PHR based diabetes index service model using life behavior analysis. Wireless Personal Communications, 93(1), 161–174.

    Article  Google Scholar 

  26. Kim, J. C., Jung, H., & Chung, K. (2016). Mining based urban climate disaster index service according to potential risk. Wireless Personal Communications, 89(3), 1009–1025.

    Article  Google Scholar 

  27. Chung, K. Y., & Lee, J. H. (2004). User preference mining through hybrid collaborative filtering and content-based filtering in recommendation system. IEICE Transaction on Information and Systems, E87-D(12), 2781–2790.

    Google Scholar 

  28. Chung, K., Yoo, H., & Choe, D. E. (2018). Ambient context-based modeling for health risk assessment using deep neural network. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-018-1033-7.

    Google Scholar 

  29. Kim, J. C., & Chung, K. (2018). Neural-network based adaptive context prediction model for ambient intelligence. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-018-0972-3.

    Google Scholar 

  30. Yoo, H., & Chung, K. (2018). Mining-based lifecare recommendation using peer-to-peer dataset and adaptive decision feedback. Peer-to-Peer Networking and Applications, 11(6), 1309–1320.

    Google Scholar 

Download references

Acknowledgements

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. NRF-2016R1D1A1A09917313). Additionally, this work was supported by Kyonggi University’s Graduate Research Assistantship 2018.

Author information

Authors and Affiliations

  1. Data Mining Lab, Department of Computer Science, Kyonggi University, 154-42, Gwanggyosan-ro, Yeongtong-gu, Suwon-Si, Gyeonggi-do, 16227, South Korea

    Joo-Chang Kim

  2. Division of Computer Science and Engineering, Kyonggi University, 154-42, Gwanggyosan-ro, Yeongtong-gu, Suwon-Si, Gyeonggi-do, 16227, South Korea

    Kyungyong Chung

Authors
  1. Joo-Chang Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kyungyong Chung
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kyungyong Chung.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, JC., Chung, K. Mining Based Time-Series Sleeping Pattern Analysis for Life Big-Data. Wireless Pers Commun 105, 475–489 (2019). https://doi.org/10.1007/s11277-018-5983-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-018-5983-z

Keywords

Search

Navigation