Automated Habit Detection System: A Feasibility Study

  • Hiroki Misawa
  • Takashi Obara
  • Hitoshi IyatomiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9475)


In this paper, we propose an automated habit detection system. We define a “habit” in this study as some motion that is significantly different from our common behaviors. The behaviors of two subjects during conversation are tracked by the Kinect sensor and their skeletal and facial conformations are detected. The proposed system detects the motions considered as habits by analyzing them using a principal component analysis (PCA) and wavelet multi-resolution analysis (MRA). In our experiments, we prepare a total of 108 movies containing 5 min of conversation. Of these, 100 movies are used to build the average motion model (AMM), and the remainder are used for the evaluation. The accuracy of habit detection in the proposed system is shown to have a precision of 84.0 \(\%\) and a recall of 81.8 \(\%\).


  1. 1.
    Merabian, A.: Communication without words. Psychol. Today 2, 53–55 (1968)Google Scholar
  2. 2.
    Sagawa, K., Abo, S., Tsukamoto, T., Kondo, I.: Forearm trajectory measurement during pitching motion using an elbow-mounted sensor. J. Adv. Mech. Des. Syst. Manuf. 3, 299–311 (2009)Google Scholar
  3. 3.
    Bobick, A.F., Davis, J.W.: The recognition of human movement using temporal templates. IEEE Trans. Pattern Anal. Mach. Intell. 23, 257–267 (2001)CrossRefGoogle Scholar
  4. 4.
    Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local SVM approach. In: Proceedings of the 17th International Conference on Pattern Recognition (ICPR), vol. 3, pp. 32–36 (2004)Google Scholar
  5. 5.
    Xia, L., Chen, C.C., Aggarwal, J.K.: View invariant human action recognition using histograms of 3D joints. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 20–27 (2012)Google Scholar
  6. 6.
    Evangelidis, G., Singh, G., Horaud, R.: Skeletal quads: human action recognition using joint quadruples. In: 2014 22nd International Conference on Pattern Recognition (ICPR), pp. 4513–4518 (2014)Google Scholar
  7. 7.
    Miranda, L., Vieira, T., Martinez, D., Lewiner, T., Vieira, A.W., Campos, M.F.M.: Online gesture recognition from pose kernel learning and decision forests. Pattern Recogn. Lett. 39, 65–73 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Applied Informatics, Graduate School of Science and EngineeringHosei UniversityTokyoJapan

Personalised recommendations