A Cloud-Based Intelligent Computing System for Contextual Exploration on Personal Sleep-Tracking Data Using Association Rule Mining

  • Zilu LiangEmail author
  • Bernd Ploderer
  • Mario Alberto Chapa Martell
  • Takuichi Nishimura
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 597)


With the development of wearable and mobile computing technology, more and more people start using sleep-tracking tools to collect personal sleep data on a daily basis aiming at understanding and improving their sleep. While sleep quality is influenced by many factors in a person’s lifestyle context, such as exercise, diet and steps walked, existing tools simply visualize sleep data per se on a dashboard rather than analyse those data in combination with contextual factors. Hence many people find it difficult to make sense of their sleep data. In this paper, we present a cloud-based intelligent computing system named SleepExplorer that incorporates sleep domain knowledge and association rule mining for automated analysis on personal sleep data in light of contextual factors. Experiments show that the same contextual factors can play a distinct role in sleep of different people, and SleepExplorer could help users discover factors that are most relevant to their personal sleep.


Association rules Data mining Personal informatics Sleep tracking Web applications Health Automated data analytics 



This study was supported by Australian Government Endeavour Research Fellowship and Microsoft BizSpark. We would like to thank Prof. James Bailey, Prof. Lars Kulik, Dr. Walter Karlen, Ms. Wanyu Liu, Dr. Yuxuan Li, Dr. Huizhi Elly Liang, and Mr. Yuan Li for their support and valuable feedback on this study.


  1. 1.
    Liu, W., Ploderer, W., Hoang, T.: In bed with technology: challenges and opportunities for sleep tracking. In: Proceedings of the Australian Computer-Human Interaction Conference (OzCHI 2015), pp. 142–151, Melbourne, Australia (2015)Google Scholar
  2. 2.
    Mindell, J.A., Meltzer, L.J., Carskadon, M.A., Chervin, R.D.: Developmental aspects of sleep hygiene: findings from the 2004 national sleep foundation sleep in America poll. Sleep Med. 10(7), 771–779 (2009)CrossRefGoogle Scholar
  3. 3.
    Poelstra, P.A.: Relationship between physical, psychological, social, and environmental variables and subjective sleep quality. Sleep 7(3), 255–260 (1984)Google Scholar
  4. 4.
    Liang, Z., Liu, W., Bernd, P., et al.: Making sense of personal sleep-tracking data through automated correlation analysis and visualization of sleep data and contextual information. In: Proceedings of the International Workshop on Healthy Aging Technology Mashup Service, Data and People, Shinagawa, Japan (2015)Google Scholar
  5. 5.
    Molenaar, P.C.M.: A manifesto on psychology as idiographic science bringing the person back into scientific psychology, this time forever. Measur.: Interdisc. Res. Perspect. 2, 201–218 (2004)Google Scholar
  6. 6.
    Ancker, J.S., Kaufman, D.: Rethinking health numeracy: a multidisciplinary literature review. J. Am. Med. Inform. Assoc. 14(6), 713–721 (2007)CrossRefGoogle Scholar
  7. 7.
    Buysse, D.J., Reynolds, C.F., Monk, T.H., Berman, S.R., Kupfer, D.J.: The pittsburgh sleep quality index: a new instrument for psychiatric practice and research. Psychiatry Res. 28(2), 193–213 (1989)CrossRefGoogle Scholar
  8. 8.
    Hublin, C., Partinen, M., Koskenvuo, M., Kaprio, J.: Sleep and mortality: a population-based 22-year follow-up study. Sleep 30(10), 1245 (2007)Google Scholar
  9. 9.
    Closs, S.J.: Assessment of sleep in hospital patients: a review of methods. J. Adv. Nurs. 13(4), 501–510 (1988)CrossRefGoogle Scholar
  10. 10.
    Morgenthaler, T., Alessi, C., Friedman, L., et al.: Practice parameters for the use of actigraphy in the assessment of sleep and sleep disorders: an update for 2007. Sleep 30(4), 519–529 (2007)Google Scholar
  11. 11.
    Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data - SIGMOD 1993, p. 207 (1993)Google Scholar
  12. 12.
    Cooley, R., Mobasher, B., Srivastava, J.: Data preparation for mining worldwide web browsing patterns. Knowl. Inf. Syst. 1, 5–32 (1999)CrossRefGoogle Scholar
  13. 13.
    Tajbakhsh, A., Rahmati, M., Mirzaei, A.: Intrusion detection using fuzzy association rules. Appl. Soft. Comput. 462–469 (2009)Google Scholar
  14. 14.
    Creighton, C., Hanash, S.: Mining gene expression databases for association rules. Bioinformatics 19(1), 79–86 (2003)CrossRefGoogle Scholar
  15. 15.
    Brin, S., Motwani, R., Ullman, J.D., Tsur, S.: Dynamic itemset counting and implication rules for market basket data. In: Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD 1997), pp. 265−276, Arizona, USA (1997)Google Scholar
  16. 16.
    Omiecinski, E.R.: Alternative interest measures for mining associations in databases. IEEE Trans. Knowl. Data Eng. 15(1), 57–69 (2003)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Aggarwal, C.C., Yu, P.S.: A new framework for itemset generation. In: Proceedings of Symposium on Principles of Database Systems, pp. 18−24, Seattle, WA, USA (1998)Google Scholar
  18. 18.
    Piatetsky-Shapiro, G.: Discovery, analysis, and presentation of strong rules. Knowledge Discovery in Databases 229–248 (1991)Google Scholar
  19. 19.
    Tan, P.N., Kumar, V., Srivastava, J.: Selecting the right interestingness measure for association patterns. In: Proceedings of SIGKDD, pp. 32–41, Canada (2002)Google Scholar
  20. 20.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), pp. 487–499, Santiago, Chile (1994)Google Scholar
  21. 21.
    Zaki, M.J.: Scalable algorithms for association mining. IEEE Trans. Knowl. Data Eng. 12(3), 372–390 (2000)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Han, J.: Mining frequent patterns without candidate generation. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 1–12 (2000)Google Scholar
  23. 23.
    Krasner, G.E., Pope, S.T.: A cookbook for using the model-view controller user interface paradigm in Smalltalk-80. J. Object Oriented Program. 1(3), 26–49 (1988)Google Scholar
  24. 24.
    D3.js data visualization library (2015). Accessed 26th December 2015
  25. 25.
    Liang, Z., Chapa-Martell, M.A.: Framing self-quantification for individual-level preventive health care. In: Proceedings of the International Conference on Health Informatics, pp. 336–343 (2015)Google Scholar
  26. 26.
    Kudyba, S.P.: Healthcare Informatics: Improving Efficiency and Productivity. CRC Press, Boca Raton (2010)CrossRefGoogle Scholar
  27. 27.
    Choe, E.K., Lee, B., Kay, M., Pratt, W., Kientz, J.A.: SleepTight: low-burden, self-monitoring technology for capturing and reflecting on sleep behaviors. In: Proceedings of UbiComp 2015, pp. 121–132, Osaka, Japan (2015)Google Scholar
  28. 28.
    Silberschatz, A., Tuzhilin, A.: What makes patterns interesting in knowledge discovery systems? IEEE Trans. Know. Data Eng. 8(6), 970–974 (1996)CrossRefGoogle Scholar
  29. 29.
    Taylor, S.E.: Asymmetrical effects of positive and negative events: the mobilization-minimization hypothesis. Psychol. Bull. 110(1), 67 (1991)CrossRefGoogle Scholar
  30. 30.
    Hall, M.H., Okun, M.L., Atwood, C.W., Buysse, D.J., et al.: Measurement of sleep by polysomnography. In: Handbook of Physiological Research Methods in Health Psychology, pp. 341–367. Sage Publications (2008)Google Scholar
  31. 31.
    Buysse, D.J.: Sleep health: can we define it? Does it matter? Sleep 37(1), 9–17 (2014)Google Scholar
  32. 32.
    Baker, F.C., Maloney, S., Driver, H.S.: A comparison of subjective estimates of sleep with objective polysomnographic data in healthy men and women. J. Psychosom. Res. 47(4), 335–341 (1999)CrossRefGoogle Scholar
  33. 33.
    Watson, N.F., Badr, M.S., Belenky, G., et al.: Joint consensus statement of the American academy of sleep medicine and sleep research society on the recommended amount of sleep for a healthy adult: methodology and discussion. Sleep 38(8), 1161–1183 (2015)Google Scholar
  34. 34.
    Engle-Friedman, M., Bootzin, R.R., Hazlewood, L., Tsao, C.: An evaluation of behavioral treatments for insomnia in the older adults. J. Clin. Psychol. 48, 77–90 (1992)CrossRefGoogle Scholar
  35. 35.
    Lichstein, K.L., Durrence, H.H., Taylor, D.J., et al.: Quantitative criteria for insomnia. Behav. Res. Ther. 41, 427–445 (2003)CrossRefGoogle Scholar
  36. 36.
    Carskadon, M.A., Dement, W.C.: Normal human sleep: an overview. In: Kryger, M.H., Roth, T., Dement, W.C. (eds.) Principles and Practice of Sleep Medicine. 4th ed, pp. 13–23. Elsevier Saunders, Philadelphia (2005)Google Scholar
  37. 37.
    Chennaoui, M., et al.: Sleep and exercise: a reciprocal issue? Sleep Med. Rev. 20, 59–72 (2014)CrossRefGoogle Scholar
  38. 38.
    Kravitz, R.L., Duan, N. (eds.) and the DEcIDE Methods Center N-of-1 Guidance Panel. Design and Implementation of N-of-1 Trials: A User’s Guide. AHRQ Publication No. 13(14)-EHC122-EF. Rockville, MD: Agency for Healthcare Research and Quality (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Zilu Liang
    • 1
    • 2
    • 3
    Email author
  • Bernd Ploderer
    • 1
  • Mario Alberto Chapa Martell
    • 2
  • Takuichi Nishimura
    • 3
  1. 1.Department of Computing and Information SystemsUniversity of MelbourneMelbourneAustralia
  2. 2.Department of Electrical Engineering and Information SystemsThe University of TokyoTokyoJapan
  3. 3.Human Informatics Research InstituteNational Institute of Advanced Industrial Science and TechnologyTsukubaJapan

Personalised recommendations