Author(s):
- Guo, Li
Abstract:
The recent development of smartphone and wearable sensor technologies enable general public to carry self-tracking tasks more easily. Much work has been devoted to life data collection and visualisation to help people with better self-understanding. We believe that although (awareness/knowledge discovery is an important aspect of personal informatics, knowledge maintenance is more, or at least equally, important. In this paper, we propose a proactive approach that uses the knowledge mined from people’s activity data to nudge them towards a good lifestyle (better knowledge maintenance). For demonstration purpose, a trial study was designed and implemented for good sleep maintenance. In the study, we first use smartphones as activity trackers to collect various features in a non-intrusive manner. We then use those data to learn users’ activity patterns, including daily step amount, app usages, bedding time, wake-up time and sleep duration. Subsequently, we analyse correlations that may have the positive or negative impact on users’ sleep qualities and finally we designed and implemented three proactive services that are able to generate customised advice in the “right” context to nudge users towards a better lifestyle. The experiments results are positive showing that with the use of the proposed services 1. daily step amount have been increased by 3.03% on average in a 10 days study and 2. sleep durations are increased by 7% for two subjects.
Document:
https://arxiv.org/abs/1610.00460
References:
[1] I. Li, A. K. Dey and J. Forlizzi, “Understanding My Data, Myself: Supporting Self-reflection with Ubicomp Technologies,” in Proceedings of the 13th International Conference on Ubiquitous Computing, New York, NY, USA, 2011.
[2] M. Swan, “The quantified self: Fundamental disruption in big data science and biological discovery,” Big Data, vol. 1, no. 2, pp. 85-99, 2013.
[3] I. Li, A. Dey and J. Forlizzi, “A stage-based model of personal informatics systems,” in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2010.
[4] J. Rooksby, M. Rost, A. Morrison and M. C. Chalmers, “Personal tracking as lived informatics,” in Proceedings of the 32nd annual ACM conference on Human factors in computing systems, 2014.
[5] J. Kopp, “Self-monitoring: A literature review of research and practice,” in Social Work Research and Abstracts, 1988.
[6] E. K. Choe, N. B. Lee, B. Lee, W. Pratt and J. A. Kientz, “Understanding quantified-selfers’ practices in collecting and exploring personal data,” in Proceedings of the 32nd annual ACM conference on Human factors in computing systems, 2014.
[7] G. Dworkin, “Paternalism,” the Monist, pp. 64-84, 1972.
[8] P. C. Shih, K. Han, E. S. Poole, M. B. Rosson and J. M. Carroll, “Use and adoption challenges of wearable activity trackers,” iConference 2015 Proceedings, 2015.
[9] M. Bazerman and D. A. Moore, “Judgment in managerial decision making,” 2012.
[10] Apple Watch. https://www.apple.com/uk/watch/.
[11] FitBit. http://www.fitbit.com.
[12] Jawbone. https://jawbone.com/.
[13] Nike+. https://secure-nikeplus.nike.com/plus/.
[14] Z. Chen, M. Lin, F. Chen, N. Lane, G. Cardone, R. Wang, T. Li, Y. Chen, T. Choudhury and A. Campbell, “Unobtrusive sleep monitoring using smartphones,” in Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2013 7th International Conference on, 2013.
[15] J.-K. Min, A. Doryab, J. Wiese, S. Amini, J. Zimmerman and J. I. Hong, “Toss ‘N’ Turn: Smartphone As Sleep and Sleep Quality Detector,” in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, New York, NY, USA, 2014.
[16] XinBao Heart Care. http://www.zettasense.co.uk.
[17] AliveCor. http://www.alivecor.com/home.
[18] Glooko. https://glooko.com/.
[19] Withings. http://www.withings.com/.
[20] M. Swan, “Health 2050: the realization of personalized medicine through crowdsourcing, the Quantified Self, and the participatory biocitizen,” Journal of Personalized Medicine, vol. 2, no. 3, pp. 93-118, 2012.
[21] M. Roantree, J. Shi, P. Cappellari, M.F. O’Connor, M. Whelan and N. Moyna, “Data transformation and query management in personal health sensor networks,” Journal of Network and Computer Applications, vol. 35, no. 4, pp. 1191-1202, 2012.
[22] F. Bentley, K. Tollmar, P. Stephenson, L. Levy, B. Jones, S. Robertson, E. Price, R. Catrambone and J. Wilson, “Health Mashups: Presenting Statistical Patterns Between Wellbeing Data and Context in Natural Language to Promote Behavior Change,” ACM Trans. Comput.-Hum. Interact., vol. 20, no. 5, pp. 30:1–30:27, #nov# 2013.
[23] N. Kamal, S. Fels and K. Ho, “Online Social Networks for Personal Informatics to Promote Positive Health Behavior,” in Proceedings of Second ACM SIGMM Workshop on Social Media, New York, NY, USA, 2010.
[24] Ingress. https://play.google.com/store/apps/details?id=com.nianticproject.ingress\&hl=en_GB.
[25] BallStrike. http://www.fit-master.com/.
[26] ZombiesRun. https://www.zombiesrungame.com/.
[27] Sleep as Android. https://sites.google.com/site/sleepasandroid/.
[28] R. H. Thaler and C. R. Sunstein, Nudge: Improving decisions about health, wealth, and happiness, Yale University Press, 2008.
[29] P. Basham, “Are nudging and shoving good for public health,” A Democracy Institute Report: http://tinyurl. com/4m6j6m9, 2010.
[30] S. Vallg{\aa}rda, “Nudge—A new and better way to improve health?,” Health policy, vol. 104, no. 2, pp. 200-203, 2012.
[31] A. Rapp, “Beyond gamification: Enhancing user engagement through meaningful game elements.,” in FDG, 2013.
[32] A. Tversky and D. Kahneman, “Judgment under uncertainty: Heuristics and biases,” science, vol. 185, no. 4157, pp. 1124-1131, 1974.
[33] Y. Li, L. Guo, C. Wu, C.-H. Lee and Y. Guo, “Building a cloud-based platform for personal health sensor data management,” in Biomedical and Health Informatics (BHI), 2014 IEEE-EMBS International Conference on, 2014.
[34] Y. Li, C. Wu, L. Guo, C.-H. Lee and Y. Guo, “Wiki-Health: A Big Data Platform for Health,” Cloud Computing Applications for Quality Health Care Delivery, p. 59, 2014.
[35] A. Theadom and M. Cropley, “‘This constant being woken up is the worst thing’–experiences of sleep in fibromyalgia syndrome,” Disability and rehabilitation, vol. 32, no. 23, pp. 1939-1947, 2010.
[36] L. Chen, M. T. {\”O}zsu and V. Oria, “Robust and fast similarity search for moving object trajectories,” in Proceedings of the 2005 ACM SIGMOD international conference on Management of data, 2005.
[37] S. Dodge, P. Laube and R. Weibel, “Movement similarity assessment using symbolic representation of trajectories,” International Journal of Geographical Information Science, vol. 26, no. 9, pp. 1563-1588, 2012.
[38] G. Trajcevski, H. Ding, P. Scheuermann, R. Tamassia and D. Vaccaro, “Dynamics-aware similarity of moving objects trajectories,” in Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems, 2007.
[39] Google Place Service. https://developers.google.com/places/documentation/.
[40] M. Choliz, “Mobile phone addiction: a point of issue,” Addiction, vol. 105, no. 2, pp. 373-374, 2010.
[41] R. M. Ryan and E. L. Deci, “Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being.,” American psychologist, vol. 55, no. 1, p. 68, 2000.
[42] M. Kay, E. K. Choe, J. Shepherd, B. Greenstein, N. Watson,S. Consolvo and J. A. Kientz, “Lullaby: A Capture \&\#38; Access System for Understanding the Sleep Environment,” in Proceedings of the 2012 ACM Conference on Ubiquitous Computing, New York, NY, USA, 2012.
[43] M. Morris and F. Guilak, “Mobile Heart Health: Project Highlight,” IEEE Pervasive Computing, vol. 8, no. 2, pp. 57-61, #apr# 2009.
[44] S. Consolvo, D. W. McDonald, T. Toscos, M. Y. Chen, J. Froehlich, B. Harrison, P. Klasnja, A. LaMarca, L. LeGrand, R.Libby, I. Smith and J. A. Landay, “Activity Sensing in the Wild: A Field Trial of Ubifit Garden,” in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, New York, NY, USA, 2008.
[45] J. J. Lin, L. Mamykina, S. Lindtner, G. Delajoux and H. B. Strub, “Fish’N’Steps: Encouraging Physical Activity with an Interactive Computer Game,” in Proceedings of the 8th International Conference on Ubiquitous Computing, Berlin, Heidelberg, 2006.
[46] G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions,” Knowledge and Data Engineering, IEEE Transactions on, vol. 17, no. 6, pp. 734-749, June 2005.
[47] J. Heckhausen and C. S. Dweck, Motivation and self-regulation across the life span, Cambridge University Press, 1998.
[48] T. C. Leonard, “Richard H. Thaler, Cass R. Sunstein, Nudge: Improving decisions about health, wealth, and happiness,” Constitutional Political Economy, vol. 19, no. 4, pp. 356-360, 2008.