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Plantar Pressure Data Based Gait Recognition by Using Long Short-Term Memory Network

  • Xiaopeng Li
  • Yuqing He
  • Xiaodian Zhang
  • Qian Zhao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10996)

Abstract

As a kind of continuous time series, plantar pressure data contains rich contact of time information which has not been fully utilized in existing gait recognition methods. In this paper, we proposed a new gait recognition method based on plantar pressure data with a Long Short-Term Memory (LSTM) network. By normalization and dimensionality reduction, the raw pressure data was converted to feature tensor. Then we feed the LSTM network with the feature tensors and implement classification recognition. We collected data from 93 subjects of different age groups, and each subjects was collected 10 sets of pressure data. The experiment results turn out that our LSTM network can get high classification accuracy and performs better than CNN model and many traditional methods.

Keywords

Gait recognition LSTM Plantar pressure data 

References

  1. 1.
    Ben, X., Meng, W., Yan, R., Wang, K.: An improved biometrics technique based on metric learning approach. Neurocomputing 97, 44–51 (2012)CrossRefGoogle Scholar
  2. 2.
    Ben, X., Zhang, P., Meng, W., Yan, R., Yang, M., Liu, W., Zhang, H.: On the distance metric learning between cross-domain gaits. Neurocomputing 208, 153–164 (2016)CrossRefGoogle Scholar
  3. 3.
    Pujol, E., Müller, B., Coll, R., et al.: Gait pattern recognition by foot pressure measurement in patients with intra-articular calcaneus fractures. In: World Congress of the International Society of Physical and Rehabilitation Medicine (2009)Google Scholar
  4. 4.
    Feng, Y., Li, Y., Luo, J.: Learning effective gait features using LSTM. In: 23rd International Conference on Pattern Recognition, ICPR 2016, Cancún, Mexico, 4–8 December 2016, pp. 325–330 (2016)Google Scholar
  5. 5.
    Xia, R., Ma, Z., Yao, Z., Sun, Y.: Gait recognition based on spatio-temporal HOG feature of plantar pressure distribution. J.PR & AI. 26(6), 529–536 (2013)Google Scholar
  6. 6.
    Pataky, T.C., Mu, T., Bosch, K., Rosenbaum, D., Goulermas, J.Y.: Gait recognition: highly unique dynamic plantar pressure patterns among 104 individuals. J. R. Soc. Interface 9, 790–800 (2012)CrossRefGoogle Scholar
  7. 7.
    Li, Y., et al.: A convolutional neural network for gait recognition based on plantar pressure images. In: Zhou, J., et al. (eds.) CCBR 2017. LNCS, vol. 10568, pp. 466–473. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-69923-3_50CrossRefGoogle Scholar
  8. 8.
    Greff, K., Srivastava, R., Koutník, J., et al.: LSTM: a search space odyssey. IEEE Trans. Neural Netw. Learn. Syst. 28(10), 2222–2232 (2016)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Liao, R., Cao, C., Garcia, E.B., Yu, S., Huang, Y.: Pose-based temporal-spatial network (PTSN) for gait recognition with carrying and clothing variations. In: Zhou, J., et al. (eds.) CCBR 2017. LNCS, vol. 10568, pp. 474–483. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-69923-3_51CrossRefGoogle Scholar
  10. 10.
    Xie, C., Li, C., Zhang, B., Chen, C.: Memory attention networks for skeleton-based action recognition. IJCAI (2018). arXiv: 1804.08254Google Scholar
  11. 11.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  12. 12.
    Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: Advances in Neural Information Processing Systems, pp. 568–576 (2014)Google Scholar
  13. 13.
    Zhang, K., Huang, Y., Du, Y., et al.: Facial expression recognition based on deep evolutional spatial-temporal networks. IEEE Trans. Image Process. 26, 4193–4203 (2017)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Liu, Y.-H., Liu, X., Fan, W., Zhong, B., Du, J.-X.: Efficient audio-visual speaker recognition via deep heterogeneous feature fusion. In: Zhou, J., et al. (eds.) CCBR 2017. LNCS, vol. 10568, pp. 575–583. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-69923-3_62CrossRefGoogle Scholar
  15. 15.
    Xia, Y., Ma, Z., Yao, Z., Sun, Y.: Gait recognition based on spatio-temporal HOG of plantar pressure distribution. Pattern Recognit. Artif. Intell. 26, 529–536 (2013)Google Scholar
  16. 16.
    Huang, H., Qiu, J., Liu, T., et al.: Similarity of center of pressure progression during walking and jogging of anterior cruciate ligament deficient patients. Plos One 12(1) (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Xiaopeng Li
    • 1
  • Yuqing He
    • 1
  • Xiaodian Zhang
    • 1
  • Qian Zhao
    • 1
  1. 1.Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education of China, School of OptoelectronicsBeijing Institute of TechnologyBeijingChina

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