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

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Intelligent Computing Systems (ISICS 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 597))

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Abstract

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.

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References

  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. 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)

    Article  Google Scholar 

  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. 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. 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. Ancker, J.S., Kaufman, D.: Rethinking health numeracy: a multidisciplinary literature review. J. Am. Med. Inform. Assoc. 14(6), 713–721 (2007)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. Closs, S.J.: Assessment of sleep in hospital patients: a review of methods. J. Adv. Nurs. 13(4), 501–510 (1988)

    Article  Google Scholar 

  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. 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. Cooley, R., Mobasher, B., Srivastava, J.: Data preparation for mining worldwide web browsing patterns. Knowl. Inf. Syst. 1, 5–32 (1999)

    Article  Google Scholar 

  13. Tajbakhsh, A., Rahmati, M., Mirzaei, A.: Intrusion detection using fuzzy association rules. Appl. Soft. Comput. 462–469 (2009)

    Google Scholar 

  14. Creighton, C., Hanash, S.: Mining gene expression databases for association rules. Bioinformatics 19(1), 79–86 (2003)

    Article  Google Scholar 

  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. Omiecinski, E.R.: Alternative interest measures for mining associations in databases. IEEE Trans. Knowl. Data Eng. 15(1), 57–69 (2003)

    Article  MathSciNet  Google Scholar 

  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. Piatetsky-Shapiro, G.: Discovery, analysis, and presentation of strong rules. Knowledge Discovery in Databases 229–248 (1991)

    Google Scholar 

  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. 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. Zaki, M.J.: Scalable algorithms for association mining. IEEE Trans. Knowl. Data Eng. 12(3), 372–390 (2000)

    Article  MathSciNet  Google Scholar 

  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. 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. D3.js data visualization library (2015). http://d3js.org. Accessed 26th December 2015

  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. Kudyba, S.P.: Healthcare Informatics: Improving Efficiency and Productivity. CRC Press, Boca Raton (2010)

    Book  Google Scholar 

  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. Silberschatz, A., Tuzhilin, A.: What makes patterns interesting in knowledge discovery systems? IEEE Trans. Know. Data Eng. 8(6), 970–974 (1996)

    Article  Google Scholar 

  29. Taylor, S.E.: Asymmetrical effects of positive and negative events: the mobilization-minimization hypothesis. Psychol. Bull. 110(1), 67 (1991)

    Article  Google Scholar 

  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. Buysse, D.J.: Sleep health: can we define it? Does it matter? Sleep 37(1), 9–17 (2014)

    Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  35. Lichstein, K.L., Durrence, H.H., Taylor, D.J., et al.: Quantitative criteria for insomnia. Behav. Res. Ther. 41, 427–445 (2003)

    Article  Google Scholar 

  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. Chennaoui, M., et al.: Sleep and exercise: a reciprocal issue? Sleep Med. Rev. 20, 59–72 (2014)

    Article  Google Scholar 

  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 

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Acknowledgment

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.

Author information

Authors and Affiliations

  1. Department of Computing and Information Systems, University of Melbourne, Melbourne, Australia

    Zilu Liang & Bernd Ploderer

  2. Department of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo, Japan

    Zilu Liang & Mario Alberto Chapa Martell

  3. Human Informatics Research Institute, National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan

    Zilu Liang & Takuichi Nishimura

Authors
  1. Zilu Liang
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  2. Bernd Ploderer
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  3. Mario Alberto Chapa Martell
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  4. Takuichi Nishimura
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Correspondence to Zilu Liang .

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Editors and Affiliations

  1. Universidad Autónoma de Yucatán, Mérida, Yucatán, Mexico

    Anabel Martin-Gonzalez

  2. Universidad Autónoma de Yucatán, Mérida, Yucatán, Mexico

    Victor Uc-Cetina

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Liang, Z., Ploderer, B., Martell, M.A.C., Nishimura, T. (2016). A Cloud-Based Intelligent Computing System for Contextual Exploration on Personal Sleep-Tracking Data Using Association Rule Mining. In: Martin-Gonzalez, A., Uc-Cetina, V. (eds) Intelligent Computing Systems. ISICS 2016. Communications in Computer and Information Science, vol 597. Springer, Cham. https://doi.org/10.1007/978-3-319-30447-2_7

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  • DOI: https://doi.org/10.1007/978-3-319-30447-2_7

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-30447-2

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