ABSTRACT
The bedroom environment can have a significant impact on the quality of a person's sleep. Experts recommend sleeping in a room that is cool, dark, quiet, and free from disruptors to ensure the best quality sleep. However, it is sometimes difficult for a person to assess which factors in the environment may be causing disrupted sleep. In this paper, we present the design, implementation, and initial evaluation of a capture and access system, called Lullaby. Lullaby combines temperature, light, and motion sensors, audio and photos, and an off-the-shelf sleep sensor to provide a comprehensive recording of a person's sleep. Lullaby allows users to review graphs and access recordings of factors relating to their sleep quality and environmental conditions to look for trends and potential causes of sleep disruptions. In this paper, we report results of a feasibility study where participants (N=4) used Lullaby in their homes for two weeks. Based on our experiences, we discuss design insights for sleep technologies, capture and access applications, and personal informatics tools.
- Abowd, G. Classroom 2000: An experiment with the instrumentation of a living educational environment. IBM Systems Journal, (1999), 1--52. Google ScholarDigital Library
- Allen, D. W. and Ryan, K. Microteaching. Addison-Wesley Pub. Co., 1969.Google Scholar
- Basner, M., Müller, U., and Elmenhorst, E. M. Single and combined effects of air, road, and rail traffic noise on sleep and recuperation. Sleep 34, 1 (2011), 11.Google ScholarCross Ref
- Buysse, D. J., Reynolds, C. F., Monk, T. H., Berman, S. R., and Kupfer, D. J. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry research 28, 2 (1989), 193--213.Google Scholar
- Campbell, M., Fitzpatrick, R., Haines, A., and Kinmonth, A. Framework for design and evaluation of complex interventions to improve health. BMJ 321, September (2000).Google Scholar
- Choe, E. K., Consolvo, S., Jung, J., Harrison, B., and Kientz, J. A. Living in a Glass House: A Survey of Private Moments in the Home. UbiComp 2011, 41--44. Google ScholarDigital Library
- Choe, E. K., Consolvo, S., Watson, N. F., and Kientz, J. A. Opportunities for Computing Technologies to Support Healthy Sleep Behaviors. CHI 2011. Google ScholarDigital Library
- Douglas, N. J., Thomas, S., and Jan, M. A. Clinical value of polysomnography. The Lancet 339, 8789 (1992), 347--350.Google ScholarCross Ref
- Gemmell, J., Bell, G., Lueder, R., Drucker, S., and Wong, C. MyLifeBits: fulfilling the Memex vision. ACM Multimedia 2002, 235--238. Google ScholarDigital Library
- Hansen, T. R. and Bardram, J. E. ActiveTheatre -- A Collaborative, Event-Based Capture and Access System for the Operating Theatre. UbiComp 2005, 375--392. Google ScholarDigital Library
- Hayes, G. R., Gardere, L. M., Abowd, G. D., and Truong, K. N. CareLog: a selective archiving tool for behavior management in schools. CHI 2008, 685--694. Google ScholarDigital Library
- Hodges, S., Williams, L., Berry, E., et al. SenseCam: A retrospective memory aid. UbiComp 2006, 177--193. Google ScholarDigital Library
- Hume, K. I. Noise pollution: a ubiquitous unrecognized disruptor of sleep? Sleep 34, 1 (2011), 7--8.Google ScholarCross Ref
- Kientz, J. KidCam: toward an effective technology for the capture of children's moments of interest. Pervasive Computing, (2009), 115--132. Google ScholarDigital Library
- Kim, S., Kientz, J. A., Patel, S. N., and Abowd, G. D. Are you sleeping?: sharing portrayed sleeping status within a social network. CSCW 2008, 619--628. Google ScholarDigital Library
- Klasnja, P., Consolvo, S., and Pratt, W. How to evaluate technologies for health behavior change in HCI research. CHI 2011, 3063. Google ScholarDigital Library
- Kozaki, T., Kitamura, S., Higashihara, Y., Ishibashi, K., Noguchi, H., and Yasukouchi, A. Effect of Color Temperature of Light Sources on Slow-wave Sleep. J Physiol Anthropol Appl Hum Sci 24, 2 (2005), 183--186.Google Scholar
- Kpanja, E. A study of the effects of video tape recording in microteaching training. BJET 32, 4 (2001), 483--486.Google Scholar
- Kryger, M. H., Roth, T., and Dement, W. C. Principles and practice of sleep medicine. W. B. Saunders Co., Philadelphia, 2000.Google Scholar
- Li, I., Dey, A., and Forlizzi, J. Using Contextual Information to Improve Awareness of Physical Activity. Engaging Data Forum 2009, (2009).Google Scholar
- Li, I., Dey, A., and Forlizzi, J. A stage-based model of personal informatics systems. CHI 2010, 557--566. Google ScholarDigital Library
- Lipford, H. R. and Abowd, G. Reviewing Meetings in TeamSpace. Human-Computer Interaction 23, 4 (2008).Google Scholar
- National Sleep Foundation. The Sleep Environment. http://www.sleepfoundation.org/article/how-sleep-works/the-sleep-environment.Google Scholar
- Perlis, M. L., Jungquist, C., Smith, M. T., and Posner, D. Cognitive Behavioral Treatment of Insomnia: A Session-by-Session Guide. Springer, 2005.Google Scholar
- Price, B. A., Petre, M., and Rogers, Y. Some Challenges in Activity and Sleep Monitoring for Personal Informatics. CHI 2011 Personal Informatics Workshop.Google Scholar
- Roy, D., Patel, R., DeCamp, P., et al. The Human Speechome Project. Symbol Grounding and Beyond, Springer Berlin Heidelberg (2006), 192--196. Google ScholarDigital Library
- Schmidt, A. Network alarm clock (The 3AD International Design Competition). Personal and Ubiquitous Computing 10, 2--3 (2005), 191--192. Google ScholarDigital Library
- Truong, K. N. and Hayes, G. R. Ubiquitous Computing for Capture and Access. Foundations and Trends in HCI 2, (2009), 95--171. Google ScholarDigital Library
- Zanobetti, A., Redline, S., Schwartz, J., et al. Associations of PM10 with Sleep and Sleep-disordered Breathing in Adults from Seven U. S. Urban Areas. Am. J. Respir. Crit. Care Med. 182, 6 (2010), 819--825.Google ScholarCross Ref
Index Terms
- Lullaby: a capture & access system for understanding the sleep environment
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