Deep convolutional neural networks on multichannel time series for human activity recognition

IJCAI International Joint Conference on Artificial Intelligence

Volumn 2015-January, Issue , 2015, Pages 3995-4001

  Yang, Jian Bo ^a   Nguyen, Minh Nhut ^a   San, Phyo Phyo ^a   Li, Xiao Li ^a   Krishnaswamy, Shonali ^a  

^a INSTITUTE FOR INFOCOMM RESEARCH   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CONVOLUTION; NETWORK ARCHITECTURE; NEURAL NETWORKS; PATTERN RECOGNITION; TIME SERIES;

ACTIVITY RECOGNITION; BENCHMARK DATASETS; CONVOLUTIONAL NEURAL NETWORK; DISCRIMINATIVE POWER; HIGHER-LEVEL ABSTRACT REPRESENTATION; HUMAN ACTIVITY RECOGNITION; SYSTEMATIC FEATURES; TIME SERIES SIGNALS;

CLASSIFICATION (OF INFORMATION);

EID: 84949810460     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (25)

1. Yoshua Bengio. Learning deep architectures for AI. Found. Trends Mach. Learn., 2(1):1-127, January 2009.
2. Andreas Bulling, Ulf Blanke, and Bernt Schiele. A tutorial on human activity recognition using body-worn inertial sensors. ACM Computing Surveys., 46(3):33:1-33:33, 2014.
3. Hong Cao, Minh Nhut Nguyen, Clifton Phua, Shonali Krishnaswamy, and Xiao Li Li. An integrated framework for human activity classific. In ACM International Conference on Ubiquitous Computing, 2012.
4. Li Deng, Jinyu Li, Jui-Ting Huang, Kaisheng Yao, Dong Yu, Frank Seide, Michael Seltzer, Geoff Zweig, Xiaodong He, Jason Williams, Yifan Gong, and Alex Acero. Recent advances in deep learning for speech research at microsoft. ICASSP, 2013.
5. Li Deng. A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Transactions on Signal and Information Processing, 2014.
6. Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell. DeCAF: A deep convolutional activation feature for generic visual recognition. In ICML, 2014.
7. Kunihiko Fukushima. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics, 36(4):193-202, 1980.
8. Geoffrey E. Hinton and Simon Osindero. A fast learning algorithm for deep belief nets. Neural Computation, 18(7):1527-1554, 2006.
9. Tâm Huynh and Bernt Schiele. Analyzing features for activity recognition. In Proceedings of the 2005 Joint Conference on Smart Objects and Ambient Intelligence: Innovative Context-aware Services: Usages and Technologies, 2005.
10. Shuiwang Ji, Wei Xu, Ming Yang, and Kai Yu. 3d convolutional neural networks for human action recognition. In ICML, 2010.
11. Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell. Caffe: Convolutional architecture for fast feature embedding. In ACM MM, 2014.
12. Eamonn Keogh and Shruti Kasetty. On the need for time series data mining benchmarks: A survey and empirical demonstration. In SIGKDD, 2002.
13. Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, 2012.
14. Q. V. Le, W. Y. Zou, S. Y. Yeung, and A. Y. Ng. Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis. CVPR, 2011.
15. Nicolas Le Roux and Yoshua Bengio. Representational power of restricted Boltzmann machines and deep belief networks. Neural Computation, 20(6):1631-1649, 2008.
16. Y. LeCun, L. Bottou, G. Orr, and K. Muller. Efficient backprop. Neural Networks: Tricks of the trade, pages 9-50, 1998.
17. Jessica Lin, Eamonn Keogh, Stefano Lonardi, and Bill Chiu. A symbolic representation of time series, with implications for streaming algorithms. In SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, 2003.
18. Thomas Plätz, Nils Y. Hammerla, and Patrick Olivier. Feature learning for activity recognition in ubiquitous computing. In IJCAI, 2012.
19. Sasank Reddy, Min Mun, Jeff Burke, Deborah Estrin, Mark Hansen, and Mani Srivastava. Using mobile phones to determine transportation modes. ACM Transactions on Sensor Networks, 6(2):13:1-13:27, March 2010.
20. Daniel Roggen, Alberto Calatroni, Mirco Rossi, Thomas Holleczek, Kilian Förster, Gerhard Tröster, Paul Lukowicz, David Bannach, Gerald Pirkl, Alois Ferscha, Jakob Doppler, Clemens Holzmann, Marc Kurz, Gerald Holl, Ricardo Chavarriaga, Marco Creatura, and José del R. Milln. Collecting complex activity data sets in highly rich networked sensor environments. In Proceedings of the Seventh International Conference on Networked Sensing Systems (INSS), Kassel, Germany, 2010.
21. Hesam et al. Sagha. Benchmarking classification techniques using the opportunity human activity dataset. In IEEE International Conference on Systems, Man, and Cybernetics, 2011.
22. Tijmen Tieleman. Training restricted boltzmann machines using approximations to the likelihood gradient. In ICML, 2008.
23. Ji Wan, Dayong Wang, Steven Chu Hong Hoi, Pengcheng Wu, Jianke Zhu, Yongdong Zhang, and Jintao Li. Deep learning for content-based image retrieval: A comprehensive study. In ACM MM, 2014.
24. Matt Zeiler and Rob Fergus. Visualizing and understanding convolutional networks. ECCV, 2014.
25. Ming Zeng, Le T. Nguyen, Bo Yu, Ole J. Mengshoel, Jiang Zhu, Pang Wu, and Joy Zhang. Convolutional neural networks for human activity recognition using mobile sensors. In MobiCASE, 2014.