Dynamic Streaming Sensor Data Segmentation for Smart Environment Applications

  • Hela SfarEmail author
  • Amel Bouzeghoub
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11306)


With the increasing availability of unobtrusive, and inexpensive sensors in smart environments, online sensor data segmentation becomes an important topic in reconstructing and understanding sensor data. Usually, in the literature, the segmentation is either performed by following a fixed or a dynamic time-window length. As stated in several works, static time-window length has several drawbacks while adjusting dynamically the window length is more appropriate. However, each of previous methods for dynamic data segmentation targets only a particular type of application. Hence, there is a need for a general method independent of applications providing high degree of usability. To achieve this aim, in this paper, we propose a novel method that dynamically adapts the time-window size. The proposal is designed to be applied in a wide range of applications (activity recognition, decision making, etc.) by combining statistical learning and semantic interpretation. This hybridization allows to analyze the incoming sensor data and choose the better time-window size. The presented approach has been implemented and evaluated in several experiments using the real dataset Aruba from the CASAS project.


Clustering Ontology Segmentation Smart environment 


  1. 1.
    Narayanan, C.K., Diane, J.C.: Activity recognition on streaming sensor data. Pervasive Mob. Comput. 10 (2014)Google Scholar
  2. 2.
    Jie, W., Michael, J.O., Gregory, M.P.O.: Dynamic sensor event segmentation for real-time activity recognition in a smart home context. Pers. Ubiquitous Comput. 19, 287–301 (2015)CrossRefGoogle Scholar
  3. 3.
    Georgo, O., Liming, C., Hui, W.: Dynamic sensor data segmentation for real-time knowledge-driven activity recognition. Pervasive Mob. Comput. 10, 155–172 (2014)CrossRefGoogle Scholar
  4. 4.
    Tak-chung, F.: A review on time series data mining. Eng. Appl. Artif. Intell. 24, 164–181 (2011)CrossRefGoogle Scholar
  5. 5.
    Nawala, Y., Belkacem, F., Anthony, F.: Towards improving feature extraction and classification for activity recognition on streaming data. J. Ambient. Intell. Hum. Comput. 8, 177–189 (2016)Google Scholar
  6. 6.
    Tian, G., Zhixian, Y.I., Karl, A.: An adaptive approach for online segmentation. In: International Workshop on Data Engineering for Wireless and Mobile Access (2012)Google Scholar
  7. 7.
    Darpan, T., Liming, C., ChenZumin, W.: Semantic segmentation of real-time sensor data stream for complex activity recognition. Pers. Ubiquitous Comput. 21, 411–425 (2017)CrossRefGoogle Scholar
  8. 8.
    Warren, T.L.: Clustering of time series data-a survey. Pattern Recogn. 38, 1857–1874 (2005)CrossRefGoogle Scholar
  9. 9.
    Wilpon, J.G., Rabiner, L.R.: Modified k-means clustering algorithm for use in isolated word recognition. IEEE Trans. Acoust. Speech Signal Process. 33 (1985)CrossRefGoogle Scholar
  10. 10.
    Tran, D., Wagner, M.: Fuzzy C-means clustering-based speaker verification. In: Pal, N.R., Sugeno, M. (eds.) AFSS 2002. LNCS (LNAI), vol. 2275, pp. 318–324. Springer, Heidelberg (2002). Scholar
  11. 11.
    Diane, J. C.: Learning setting-generalized activity models for smart spaces. IEEE Intell. Syst. 99 (2010)Google Scholar
  12. 12.
    Hearst, M.A., Dumais, S.T., Osuna, E., Platt, J., Scholkopf, B.: Support vector machines. IEEE Intell. Syst. Their Appl. 13 (1998)CrossRefGoogle Scholar
  13. 13.
    Chuanjun, L., Prabhakaran, B.: A similarity measure for motion stream segmentation and recognition. In: International Workshop on Multimedia Data Mining: Mining Integrated Media and Complex Data (2005)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.CNRS Paris Saclay, Telecom SudParis, SAMOVARÉvry, EssonneFrance

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