A Sleep Monitoring Application for u-lifecare Using Accelerometer Sensor of Smartphone

  • Muhammad Fahim
  • Le Ba Vui
  • Iram Fatima
  • Sungyoung Lee
  • Yongik Yoon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8276)


Ubiquitous lifecare (u-lifecare) is regarded as a seamless technology that can provide services to the patients as well as facilitate the healthy people to maintain an active lifestyle. In this paper, we develop a sleep monitoring application to assists the healthy people for managing their sleep. It provides an unobtrusive and proactive way for the self-management. We utilize the embedded accelerometer sensor of the smartphone as a client node to collect the sleeping data logs. Our proposed model is server-driven approach and process the data over the server machine. We classify the body movements and compute the useful sleep analytics. It facilitates the users to keep the record of daily sleep and assists to change their unhealthy sleeping habits that are identified by our computed sleep analytics such as bed time, wake up, fell asleep, body movements, frequent body movements at different stages of the night, sleep efficiency and time spent in the bed. Furthermore, we also provide our pilot study results to demonstrate the applicability with the real-world service scenarios.


Sleep Monitoring Accelerometer Sensor Smartphone u-lifecare 


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  1. 1.
    Jeong, C., Joo, S.-C., Jeong, Y.S.: Sleeping situation monitoring system in ubiquitous environments. Journal of Personal and Ubiquitous Computing, 1–8 (2012)Google Scholar
  2. 2.
    Khai, L.Q., Khoa, T.Q.D., Toi, V.V.: A tool for analysis and classification of sleep stages. In: International Conference on Advanced Technologies for Communications, pp. 307–310 (2011)Google Scholar
  3. 3.
    Gironda, R.J., Lloyd, J., Clark, M.E., Walker, R.L.: Preliminary Evaluation of the Reliability and Criterion Validity of the Actiwatch-Score. Journal of Rehabilitation Research & Development, 223–230 (2007)Google Scholar
  4. 4.
    Sleep Diary, (last visited: June 10, 2013)
  5. 5.
    Le, H.X., Lee, S., Truc, P., Vinh, L.T., Khattak, A.M., Han, M., Hung, V.D., Hassan, M.M., Kim, M., Koo, H.K., Lee, K.Y., Huh, E.N.: Secured WSN-integrated cloud computing for u-life care. In: 7th IEEE Consumer Communications and Networking Conference, pp. 1–2 (2010)Google Scholar
  6. 6.
    Adriana, M.A., Pavel, M., Tamara, L.H., Clifford, M.S.: Detection of Movement in Bed Using Unobtrusive Load Cell Sensors. IEEE Transactions on Information Technology in Biomedicine 14(2), 481–490 (2010)CrossRefGoogle Scholar
  7. 7.
    Wakemate, (last visited: June 10, 2013)
  8. 8.
    Sleep Cycle, (last visited: June 10, 2013)
  9. 9.
    Sleep as android, (last visited: June 10, 2013)
  10. 10.
    Sleep by MotionX, (last visited: June 10, 2013)
  11. 11.
    Sleepbot, (last visited: June 10, 2013)
  12. 12.
    Mizell, D.: Using gravity to estimate accelerometer orientation. In: Proceeding of the IEEE International Symposiumon Wearable Computers, Computer Society, pp. 252–253 (2003)Google Scholar
  13. 13.
    Scholkopf, B.: Advances in Kernel Methods: Support Vector Learning. MIT Press (1999) ISSBN: 9780585128290Google Scholar
  14. 14.
    Breus, M.J.: Calculating Your Perfect Bedtime and Sleep Efficiency, (last visited: June 10, 2013)

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Muhammad Fahim
    • 1
  • Le Ba Vui
    • 1
  • Iram Fatima
    • 1
  • Sungyoung Lee
    • 1
  • Yongik Yoon
    • 2
  1. 1.Ubiquitous Computing Laboratory, Department of Computer EngineeringKyung Hee UniversityKorea
  2. 2.Department of Multimedia ScienceSookmyung Womens UniversitySouth Korea

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