Inferring Complex Human Behavior Using a Non-obtrusive Mobile Sensing Platform

  • Bruce DeBruhl
  • Michele Cossalter
  • Roy Want
  • Ole Mengshoel
  • Pei Zhang
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 76)

Abstract

Thanks to the decreasing cost, increasing mobility, and wider use of sensors, a great number of possible applications have recently emerged, including applications that may impact the high-level goals in people’s lives. Applications can be found in many areas ranging from medical devices to consumer devices. Information about activity and context can be inferred from sensors and be used to provide automated recommendations.

Keywords

Activity Recognition Acceleration Data Light Sensor Wearable Sensor Triaxial Accelerometer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Coyle, E.F.: Fluid and fuel intake during exercise. Journal of Sports Sciences 22, 39–55 (2004)CrossRefGoogle Scholar
  2. 2.
    Chiu, M., Chang, S., Chang, Y., Chu, H., Chen, C.C., Hsiao, F., Ko, J.: Playful bottle: a mobile social persuasion system to motivate healthy water intake. In: Ubicomp, pp. 185–194. ACM, New York (2009)CrossRefGoogle Scholar
  3. 3.
    Ravi, N., Nikhil, D., Mysore, P., Littman, M.L.: Activity recognition from accelerometer data. In: IAAI, pp. 1541–1546. AAAI Press (2005)Google Scholar
  4. 4.
    U.S. Standard Atmosphere. U.S. Government Printing Office, Washington, D.C. (1976)Google Scholar
  5. 5.
    Cook, C.J.: High Altitude Hydration (2008), http://ezinearticles.com/?High-Altitude-Hydration&id=1216328
  6. 6.
    Steadman, R.G.: The Assessment of Sultriness. Part II: Effects of Wind, Extra Radiation and Barometric Pressure on Apparent Temperature. Journal of Applied Meteorology 18(7), 874–885 (1979)Google Scholar
  7. 7.
    Lester, J., Tan, D., Patel, S., Brush, A.J.B.: Automatic Classification of Daily Fluid Intake. In: International Conference on Pervasive Computing Technologies for Healthcare (2010)Google Scholar
  8. 8.
    Sundaram, S., Cuevas, W.W.: High level activity recognition using low resolution wearable vision. In: IEEE CVPR (2009)Google Scholar
  9. 9.
    Subramanya, A., Raj, A.: Recognizing activities and spatial context using wearable sensors. In: UAI (2006)Google Scholar
  10. 10.
    Klasnja, P., Harrison, B.L., LeGrand, L., LaMarca, A., Froehlich, J., Hudson, S.E.: Using wearable sensors and real time inference to understand human recall of routine activities. In: UbiComp, pp. 154–163. ACM, New York (2008)CrossRefGoogle Scholar
  11. 11.
    Welbourne, E., Lester, J., LaMarca, A., Borriello, G.: Mobile Context Inference Using Low-Cost Sensors. In: Strang, T., Linnhoff-Popien, C. (eds.) LoCA 2005. LNCS, vol. 3479, pp. 254–263. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2012

Authors and Affiliations

  • Bruce DeBruhl
    • 1
  • Michele Cossalter
    • 1
  • Roy Want
    • 2
  • Ole Mengshoel
    • 1
  • Pei Zhang
    • 1
  1. 1.Carnegie Mellon UniversityUSA
  2. 2.Intel LabsUSA

Personalised recommendations