Activity Recognition with Mobile Phones

  • Jordan Frank
  • Shie Mannor
  • Doina Precup
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6913)


Our demonstration consists of a working activity and gait recognition system, implemented on a commercial smartphone. The activity recognition feature allows participants to train various activities, such as running, walking, or jumping, on the phone; the system can then identify when those activities are performed. The gait recognition feature learns particular characteristics of how participants walk, allowing the phone to identify the person carrying it.


Mobile Phone Activity Recognition Dynamic Time Warping Wearable Sensor Gait Recognition 
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.


  1. 1.
    Ailisto, H., Lindholm, M., Mantyjarvi, J., Vildjiounaite, E., Makela, S.: Identifying people from gait pattern with accelerometers. In: Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol. 5779, pp. 7–14 (2005)Google Scholar
  2. 2.
    Berndt, D., Clifford, J.: Using dynamic time warping to find patterns in time series. In: AAAI 1994 Workshop on Knowledge Discovery in Databases, pp. 229–248 (1994)Google Scholar
  3. 3.
    Buzug, T., Pfister, G.: Optimal delay time and embedding dimension for delay-time coordinates by analysis of the global static and local dynamical behavior of strange attractors. Phys. Rev. A 45(10), 7073–7084 (1992)CrossRefGoogle Scholar
  4. 4.
    Cristianini, N., Shawe-Taylor, J.: An introduction to Support Vector Machines: and other kernel-based learning methods. Cambridge University Press, Cambridge (2000)CrossRefzbMATHGoogle Scholar
  5. 5.
    Frank, J., Mannor, S., Precup, D.: Activity and gait recognition with time-delay embeddings. In: Proc. of the AAAI Conference on Artificial Intelligence (2010)Google Scholar
  6. 6.
    Kantz, H., Schreiber, T.: Nonlinear time series analysis. Cambridge University Press, Cambridge (2004)zbMATHGoogle Scholar
  7. 7.
    Kennel, M., Brown, R., Abarbanel, H.: Determining embedding dimension for phase space reconstruction using the method of false nearest neighbors. Phys. Rev. A 45(6), 3403–3411 (1992)CrossRefGoogle Scholar
  8. 8.
    Lester, J., Choudhury, T., Borriello, G.: A practical approach to recognizing physical activities. In: Fishkin, K.P., Schiele, B., Nixon, P., Quigley, A. (eds.) PERVASIVE 2006. LNCS, vol. 3968, pp. 1–16. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Subramanya, A., Raj, A., Bilmes, J., Fox, D.: Recognizing activities and spatial context using wearable sensors. In: Proc. of Uncertainty in Artificial Intelligence (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jordan Frank
    • 1
  • Shie Mannor
    • 2
  • Doina Precup
    • 1
  1. 1.School of Computer ScienceMcGill UniversityMontrealCanada
  2. 2.Department of Electrical EngineeringTechnionIsrael

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