Table of Contents Author Guidelines Submit a Manuscript
Computational Intelligence and Neuroscience
Volume 2018 (2018), Article ID 4160652, 12 pages
https://doi.org/10.1155/2018/4160652
Research Article

Recurrent Transformation of Prior Knowledge Based Model for Human Motion Recognition

1School of Computer and Communication Engineering, University of Science and Technology Beijing, China
2Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, China
3Department of Electronic Engineering, National Taipei University of Technology, Taipei, Taiwan
4The Computer Network Information Center (CNIC), The Chinese Academy of Sciences (CAS), Beijing, China

Correspondence should be addressed to Jie He and Xiaotong Zhang

Received 25 July 2017; Revised 15 November 2017; Accepted 27 November 2017; Published 14 January 2018

Academic Editor: Eris Chinellato

Copyright © 2018 Cheng Xu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. X. Mao, M. Li, W. Li et al., “Progress in EEG-based brain robot interaction systems,” Computational Intelligence and Neuroscience, vol. 2017, Article ID 1742862, 25 pages, 2017. View at Publisher · View at Google Scholar
  2. S. Scheurer, S. Tedesco, K. N. Brown, and B. O'Flynn, “Human activity recognition for emergency first responders via body-worn inertial sensors,” in Proceedings of the IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN), pp. 5–8, IEEE, Eindhoven, Netherlands, May 2017. View at Publisher · View at Google Scholar
  3. A. Yassine, S. Singh, and A. Alamri, “Mining human activity patterns from smart home big data for health care applications,” IEEE Access, vol. 5, pp. 13131–13141, 2017. View at Publisher · View at Google Scholar
  4. C. Xu, J. He, X. Zhang, C. Yao, and P. Tseng, “Geometrical kinematic modeling on human motion using method of multi-sensor fusion,” Information Fusion, vol. 41, pp. 243–254, 2018. View at Publisher · View at Google Scholar
  5. T. Stiefmeier, Real-time spotting of human activities [Ph.D. thesis], TU Darmstadt, 2008.
  6. T. Poggio, H. Mhaskar, L. Rosasco, B. Miranda, and Q. Liao, “Why and when can deep-but not shallow-networks avoid the curse of dimensionality: a review,” International Journal of Automation and Computing, vol. 14, no. 5, pp. 503–519, 2017. View at Publisher · View at Google Scholar · View at Scopus
  7. K. Frank, E. M. Diaz, P. Robertson, and F. J. F. Sánchez, “Bayesian recognition of safety relevant motion activities with inertial sensors and barometer,” in Proceedings of the IEEE/ION Position, Location and Navigation Symposium (PLANS '14), pp. 174–184, May 2014. View at Publisher · View at Google Scholar · View at Scopus
  8. H. Ghasemzadeh and R. Jafari, “Physical movement monitoring using body sensor networks: A phonological approach to construct spatial decision trees,” IEEE Transactions on Industrial Informatics, vol. 7, no. 1, pp. 66–77, 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. P. H. Swain and R. C. King, “Two effective feature selection criteria for multispectral remote sensing,” 1973.
  10. H. Ghasemzadeh, E. Guenterberg, and R. Jafari, “Energy-efficient information-driven coverage for physical movement monitoring in body sensor networks,” IEEE Journal on Selected Areas in Communications, vol. 27, no. 1, pp. 58–69, 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. Z. Wang, M. Jiang, Y. Hu, and H. Li, “An incremental learning method based on probabilistic neural networks and adjustable fuzzy clustering for human activity recognition by using wearable sensors,” IEEE Transactions on Information Technology in Biomedicine, vol. 16, no. 4, pp. 691–699, 2012. View at Publisher · View at Google Scholar · View at Scopus
  12. H. Zhang, W. Yuan, Q. Shen, T. Li, and H. Chang, “A handheld inertial pedestrian navigation system with accurate step modes and device poses recognition,” IEEE Sensors Journal, vol. 15, no. 3, pp. 1421–1429, 2015. View at Publisher · View at Google Scholar · View at Scopus
  13. H. Aliakbarpour, K. Khoshhal, J. Quintas et al., “HMM-based abnormal behaviour detection using heterogeneous sensor network,” in Technological Innovation for Sustainability, vol. 349 of IFIP Advances in Information and Communication Technology, pp. 277–285, 2011. View at Publisher · View at Google Scholar
  14. C. Xu, J. He, X. Zhang, C. Wang, and S. Duan, “Detection of freezing of gait using template-matching-based approaches,” Journal of Sensors, vol. 2017, Article ID 1260734, 8 pages, 2017. View at Publisher · View at Google Scholar
  15. O. Bousquet, S. Boucheron, and G. Lugosi, “Introduction to statistical learning theory,” in Advanced Lectures on Machine Learning, vol. 3176 of Lectures Notes in Artificial Intelligence, pp. 169–207, Springer, 2004. View at Publisher · View at Google Scholar
  16. L. Chen, J. Hoey, C. D. Nugent, D. J. Cook, and Z. Yu, “Sensor-based activity recognition,” IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 42, no. 6, pp. 790–808, 2012. View at Publisher · View at Google Scholar · View at Scopus
  17. C. Chang and C. Lin, “LIBSVM: a Library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 3, article 27, 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. P. Pudil and J. Kittler, Floating search methods in feature selection, Elsevier Science, 1994.
  19. F. J. Ordóñez and D. Roggen, “Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition,” Sensors, vol. 16, no. 1, article no. 115, 2016. View at Publisher · View at Google Scholar · View at Scopus