A Recurrent Neural Network with Non-gesture Rejection Model for Recognizing Gestures with Smartphone Sensors

  • Myeong-Chun Lee
  • Sung-Bae Cho
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8251)


Gesture recognition provides a new interface to user. Various methods for the gesture recognition are feasible in smartphone environment since a number of sensors attached are gradually increasing. In this paper, we propose a gesture recognition method using smartphone accelerometer sensors. The high false-positive rate is definite if the gesture sequence data are increased. We have modified BLSTM (Bidirectional Long Short-Term Memory) recurrent neural network with non-gesture rejection model to deal with the problem. A BLSTM model classifies the input into the gesture and non-gesture classes, and the specific BLSTM models for the gestures further classify it into one of twenty gestures. 24,850 sequence data are used for the experiment, and it consists of 11,885 gesture sequences and 12,965 non-gesture sequences. The proposed method shows higher accuracy than the standard BLSTM.


Smartphone Accelerometer Sensors Gesture Spotting Gesture Recognition Recurrent Neural Network 


  1. 1.
    Lane, N.D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., Cambell, A.T.: A survey of mobile phone sensing. IEEE Communications Magazine 48(9), 140–150 (2010)CrossRefGoogle Scholar
  2. 2.
    Chen, Q., Georganas, N.D., Petriu, E.M.: Hand gesture recognition using haar-like featuresand a stochastic context-free grammar. IEEE Trans. on Instrumentation and Measurement 57(8), 1562–1571 (2008)CrossRefGoogle Scholar
  3. 3.
    Tran, C., Trivedi, M.M.: 3-D posture and gesture recognition for interactivity in smart spaces. IEEE Trans. on Industrial Informatics 8(1), 178–187 (2012)CrossRefGoogle Scholar
  4. 4.
    Zhang, X., Chen, X., Li, Y., Lantz, V., Wang, K., Yang, J.: A framework for hand gesture recognition based on accelerometer and EMG sensors. IEEE Trans. on Systems, Man, and Cybernetics, Part A: Systems and Humans 41(6), 1064–1076 (2011)CrossRefGoogle Scholar
  5. 5.
    Wheeler, K.R., Chang, M.H., Knuth, K.H.: Gesture-based control and EMG decomposition. IEEE Trans. on Systems, Man, and Cybernetics, Part C: Applications and Reviews. 36(4), 503–514 (2006)CrossRefGoogle Scholar
  6. 6.
    Xu, R., Zhou, S., Li, W.J.: MEMS accelerometer based nonspecific-user hand gesture recognition. IEEE Sensors Journal 12(5), 1166–1173 (2012)CrossRefGoogle Scholar
  7. 7.
    Liu, J., Wang, Z., Zhong, L., Wickramasuriya, J., Vasudevan, V.: uWave: accelerometer-based personalized gesture recognition and its applications. IEEE Int. Conf. on Pervasive Computing and Communications, 1–9 (2009)Google Scholar
  8. 8.
    Niezen, G., Hancke, G.P.: Evaluating and optimising accelerometer-based gesture recognition techniques for mobile devices. AFRICON, 1–6 (2009)Google Scholar
  9. 9.
    Min, J.-K., Choe, B.-W., Cho, S.-B.: A selective template matching algorithm for short and intuitive gesture UI of accelerometer-builtin mobile phones. In: Cong. on Nature and Biologically Inspired Computing, pp. 660–665 (2010)Google Scholar
  10. 10.
    Marasovic, T., Papic, V.: Accelerometer-Based Gesture Classification Using Principal Component Analysis. In: Int. Conf. on Software, Telecommunications and Computer Networks, pp. 1–5 (2011)Google Scholar
  11. 11.
    Akl, A., Feng, C., Valaee, S.: A novel accelerometer-based gesture recognition system. IEEE Trans. on Signal Processing 59(12), 6197–6205 (2011)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Hochreiter, S., Schmidhuer, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Myeong-Chun Lee
    • 1
  • Sung-Bae Cho
    • 1
  1. 1.Dept. of Computer ScienceYonsei UniversitySeoulKorea

Personalised recommendations