Abstract
There are many sleep tracking technologies in the consumer market nowadays. These technologies offer rich functions ranging from sleep pattern tracking to smart alarm clock. However, previous study indicates that users find these technologies of little use in facilitating sleep quality improvement, as simply making a user aware of how poor his/her sleep is provides no actionable information on how to improve it. Armed with such understanding, we proposed an architecture for designing intelligent sleep analysis systems and developed a prototype called SleepExplorer to help users automatically analyse and visualize the interrelationship of his/her sleep quality and the context (i.e., psychological states, physiological states, lifestyle, and environment). Such contextual information is crucial in helping users understand what the potential reasons for their sleep problems might be. We conducted a 2-week field study with 10 diverse participants, learning that SleepExplorer help users make sense of their sleep-tracking data and reflect on their lifestyle, and that the system has potentially positive impact on sleep behaviour change.
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Acknowledgement
This study was supported by Australian Government Endeavour Research Fellowship and Microsoft BizSpark. We would like to thank our study participants for their contribution. We also appreciate feedback and support from Dr. Kathleen Gray, Manal Almalki and our colleagues in Microsoft Center for Social NUI and Natural Language Processing Group, University of Melbourne.
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Liang, Z., Liu, W., Ploderer, B., Bailey, J., Kulik, L., Li, Y. (2017). Designing Intelligent Sleep Analysis Systems for Automated Contextual Exploration on Personal Sleep-Tracking Data. In: Otake, M., Kurahashi, S., Ota, Y., Satoh, K., Bekki, D. (eds) New Frontiers in Artificial Intelligence. JSAI-isAI 2015. Lecture Notes in Computer Science(), vol 10091. Springer, Cham. https://doi.org/10.1007/978-3-319-50953-2_25
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