Advertisement

Deep Learning Prediction of Gait Based on Inertial Measurements

  • Pedro Romero-Hernandez
  • Javier de Lope Asiain
  • Manuel GrañaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11486)

Abstract

We report the application of recurrent deep learning networks, namely long term short memories (LSTM) for the modeling of gait synchronization of legs using a basic configuration of off-the-shelf inertial measurement units (IMU) providing six acceleration and rotation parameters. The proposed system copes with noisy and missing data due to high sampling rate, before applying the training of LSTM. We report accurate testing results on one experimental subject. This model can be transferred to robotised prostheses and assistive robotics devices in order to achieve quick stabilization and robust transfer of control algorithms to new users.

Notes

Acknowledgements

This work has been partially supported by FEDER funds through MINECO project TIN2017-85827-P.

References

  1. 1.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  2. 2.
    Cheng, G., Peddinti, V., Povey, D., Manohar, V., Khudanpur, S., Yan, Y.: An exploration of dropout with LSTMs. In: Proceedings of Interspeech 2017 (2017)Google Scholar
  3. 3.
    Gal, Y., Ghahramani, Z.: A theoretically grounded application of dropout in recurrent neural networks. In: Advances in Neural Information Processing Systems, pp. 1019–1027 (2016)Google Scholar
  4. 4.
    Ignatov, A.: Real-time human activity recognition from accelerometer data using convolutional neural networks. Appl. Soft Comput. 62, 915–922 (2018)CrossRefGoogle Scholar
  5. 5.
    Kok, M., Hol, J. D., Sch öon, T.B.: Using inertial sensors for position and orientation estimation. arXiv preprint arXiv:1704.06053 (2017)
  6. 6.
    Ikehara, T., et al.: Development of closed-fitting-type walking assistance device for legs and evaluation of muscle activity. In: 2011 IEEE International Conference on Rehabilitation Robotics (ICORR), pp. 1–7. IEEE, June 2011Google Scholar
  7. 7.
    Chen, Y.P., Yang, J.Y., Liou, S.N., Lee, G.Y., Wang, J.S.: Online classifier construction algorithm for human activity detection using a tri-axial accelerometer. Appl. Math. Comput. 205(2), 849–860 (2008)MathSciNetGoogle Scholar
  8. 8.
    Dollar, A.M., Herr, H.: Lower extremity exoskeletons and active orthoses: challenges and state-of-the-art. IEEE Trans. Rob. 24(1), 144–158 (2008)CrossRefGoogle Scholar
  9. 9.
    Yang, J.Y., Wang, J.S., Chen, Y.P.: Using acceleration measurements for activity recognition: an effective learning algorithm for constructing neural classifiers. Pattern Recogn. Lett. 29(16), 2213–2220 (2008)CrossRefGoogle Scholar
  10. 10.
    Godfrey, A., Del Din, S., Barry, G., Mathers, J.C., Rochester, L.: Instrumenting gait with an accelerometer: a system and algorithm examination. Med. Eng. Phys. 37(4), 400–407 (2015)CrossRefGoogle Scholar
  11. 11.
    Mannini, A., Sabatini, A.M.: Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors 10(2), 1154–1175 (2010)CrossRefGoogle Scholar
  12. 12.
    Wang, J., Chen, R., Sun, X., She, M.F., Wu, Y.: Recognizing human daily activities from accelerometer signal. Proc. Eng. 15, 1780–1786 (2011)CrossRefGoogle Scholar
  13. 13.
    Erdas, C.B., Atasoy, I., Acici, K., Ogul, H.: Integrating features for accelerometer-based activity recognition. Proc. Comput. Sci. 98, 522–527 (2016)CrossRefGoogle Scholar
  14. 14.
    Garcia-Ceja, E., Brena, R.: Long-term activity recognition from accelerometer data. Proc. Technol. 7, 248–256 (2013)CrossRefGoogle Scholar
  15. 15.
    Zhang, M., Sawchuk, A.A.: A feature selection-based framework for human activity recognition using wearable multimodal sensors. In: Proceedings of the 6th International Conference on Body Area Networks, pp. 92–98. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering) (2011)Google Scholar
  16. 16.
    Bayat, A., Pomplun, M., Tran, D.A.: A study on human activity recognition using accelerometer data from smartphones. Proc. Comput. Sci. 34, 450–457 (2014)CrossRefGoogle Scholar
  17. 17.
    Lee, K., Kwan, M.P.: Physical activity classification in free-living conditions using smartphone accelerometer data and exploration of predicted results. Comput. Environ. Urban Syst. 6, 124–131 (2018)CrossRefGoogle Scholar
  18. 18.
    Wen, J., Wang, Z.: Sensor-based adaptive activity recognition with dynamically available sensors. Neurocomputing 218, 307–317 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Pedro Romero-Hernandez
    • 1
  • Javier de Lope Asiain
    • 1
  • Manuel Graña
    • 2
    Email author
  1. 1.Artificial Intelligence DepartmentMadrid Polytechnic UniversityMadridSpain
  2. 2.Computer Science DepartmentUniversity of the Basque CountrySan SebastianSpain

Personalised recommendations