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Early Detection of Hemiplegia by Analyzing the Gait Characteristics and Walking Patterns Using Convolutional Neural Networks

  • Sagar Patil
  • Akshay Shah
  • Shubham Dalvi
  • Jignesh Sisodia
Conference paper
  • 17 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1118)

Abstract

Over the time the human posture can become a cause of concern. Bad posture cannot only be caused by our living habits but can also be a symptom of neurological diseases. Various diseases can be detected and diagnosed at an early stage if the posture pattern of a person is observed and analyzed. In this paper, we devise a method utilizing deep learning and convolutional neural networks which analyzes the gait characteristics of humans to identify Hemiplegia. The method distinguishes the users as healthy or Hemiplegic based on their posture and walking pattern. The proposed method will help health professionals with the clinical examination of a patient for Hemiplegia identifications.

Keywords

Gait detection Hemiplegia Convolution neural networks Deep learning 

Notes

Acknowledgement

We thank Dr. Ganesh Salvi, Dr. Salvi’s Clinic, Mumbai, for taking the required permissions from their patients to use their video data for research purposes. The authors also thank Dr. Ganesh Salvi for their work in authenticating the Hemiplegic patient samples.

References

  1. 1.
    Krizhevsky, A., Sutskever, I., E. Hinton, G.: ImageNet classification with deep convolutional neural networks. Neural Inf. Process. Syst., vol. 25 (2012)Google Scholar
  2. 2.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: CoRR, vol. abs/1409.1556 (2014)Google Scholar
  3. 3.
    Ding, M., Fan, G.: Multilayer joint Gait-pose manifolds for human gait motion modeling. IEEE Trans. Cybern. 45(11), 2413–2424 (2015)CrossRefGoogle Scholar
  4. 4.
    Wu, Z., Huang, Y., Wang, L., Wang, X., Tan, T.: A comprehensive study on cross-view gait based human identification with deep CNNs. IEEE Trans. Pattern Anal. Mach. Intell. 39(2), 209–226 (2017)CrossRefGoogle Scholar
  5. 5.
    Takemura, N., Makihara, Y., Muramatsu, D., Echigo, T., Yagi, Y.: On input/output architectures for convolutional neural network-based cross-view gait recognition. IEEE Trans. Circuits Syst. Video Technol. 28(1) (2018)Google Scholar
  6. 6.
    Chen, S., Yang, R.: Pose Trainer: correcting exercise posture using pose estimation. Res. Gate (2018)Google Scholar
  7. 7.
    Toshev, A., Szegedy, C.: DeepPose—human pose estimation via deep neural networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (2014)Google Scholar
  8. 8.
    Ghezelghieh, M.F., Kasturi, R., Sarkar, S.: Learning camera viewpoint using CNN to improve 3D body pose estimation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 685–693 (2016)Google Scholar
  9. 9.
    Cao, Z., Hidalgo, G., Simon, T., Wei, S.E., Sheikh, Y.: OpenPose: Realtime multi-person 2D pose estimation using part affinity fields. In: CoRR, vol. abs/1812.08008 (2018)Google Scholar
  10. 10.
    Abaid, N., Cappa, P., Palermo, E., Petrarca, M., Porfiri, M.: Gait detection in children with and without Hemiplegia using single-axis wearable gyroscopes. J. Pone, pp. 0073152 (2013)Google Scholar
  11. 11.
    Lee, J., Park, S., Shin, H.: Detection of Hemiplegic walking using a wearable inertia sensing device. Journal Sensors 18, 1736 (2018)CrossRefGoogle Scholar
  12. 12.
    Li, S., Chan, A. B.: 3D Human Pose Estimation from Monocular Images with Deep Convolutional Neural Network. In: Springer International Computer Vision – ACCV 2014, pp. 332–347 (2015)Google Scholar
  13. 13.
    Bastioni, M., Re S., Misra, S.: Ideas and methods for modeling 3D human figures: the principal algorithms used by MakeHuman and their implementation in a new approach to parametric modeling. Proceedings of 1st Bangalore Annual Compute Conference (2008)Google Scholar
  14. 14.
    Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014)MathSciNetzbMATHGoogle Scholar
  15. 15.
    Sergey, I., Christian, S.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: CoRR, vol. abs/1502.03167 (2015)Google Scholar
  16. 16.
    Krizhevsky, A., Sutskever, I., E. Hinton, G.: Advances in neural information processing systems. In: ImageNet Classification with Deep Convolutional Neural Networks, pp. 1097–1105 (2012)Google Scholar
  17. 17.
    Wei, Y., Xia, W., Lin, M., Huang, J., Ni, B., Dong, J., Zhao, Y., Yan, S.: HCP—a flexible CNN framework for multi-label image classification. IEEE Trans. Pattern Anal. Mach. Intell. 38, 1901–1907 (2016)CrossRefGoogle Scholar
  18. 18.
    Deng, J., Dong, W., Socher, R., Li, L., Kai, L., Li, Fei-Fei.: ImageNet: a large-scale hierarchical image database. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops), pp. 248–255 (2009)Google Scholar
  19. 19.
    Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (2014)Google Scholar
  20. 20.
    Sebastian, R.: An overview of gradient descent optimization algorithms. In: CoRR, vol. abs/1609.04747 (2016)Google Scholar
  21. 21.
    Darken, C., Chang, J., Moody, J.: Learning rate schedules for faster stochastic gradient. In: Neural Networks for Signal Processing II Proceedings of the 1992 IEEE Workshop, Helsingoer, Denmark, pp. 3–12 (1992)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Sagar Patil
    • 1
  • Akshay Shah
    • 1
  • Shubham Dalvi
    • 1
  • Jignesh Sisodia
    • 1
  1. 1.Department of Information TechnologySardar Patel Institute of TechnologyMumbaiIndia

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