A new multi-channels sequence recognition framework using deep convolutional Neural Network

Procedia Computer Science

Volumn 53, Issue 1, 2015, Pages 383-390

  Zhang, Runfeng ^a   Li, Chunping ^a   Jia, Daoyuan ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

Deep learning; Machine learning; Sequence recognition

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIG DATA; LEARNING SYSTEMS; MARKOV PROCESSES; NEURAL NETWORKS;

CLASSIFICATION ACCURACY; CONVOLUTIONAL NEURAL NETWORK; DEEP LEARNING; DYNAMIC TIME WARPING; INTEGRATED TECHNOLOGIES; INTELLIGENT DEVICES; MACHINE LEARNING MODELS; SEQUENCE RECOGNITION;

HIDDEN MARKOV MODELS;

EID: 84939174565     PISSN: None     EISSN: 18770509     Source Type: Conference Proceeding    
DOI: 10.1016/j.procs.2015.07.315     Document Type: Conference Paper

References (8)

1. Mingyu Chen, Ghassan AlRegib, and Biing-Hwang Juang. A new 6d motion gesture database and the benchmark results of feature-based statistical recognition. In Emerging Signal Processing Applications (ESPA), 2012 IEEE International Conference on, pages 131-134. IEEE, 2012.
2. Geoffrey E Hinton, Simon Osindero, and Yee-Whye Teh. A fast learning algorithm for deep belief nets. Neural computation, 18(7):1527-1554, 2006.
3. Sanna Kallio, Juha Kela, and Jani Mäntyjärvi. Online gesture recognition system for mobile interaction. In Systems, Man and Cybernetics, 2003. IEEE International Conference on, volume 3, pages 2070-2076. IEEE, 2003.
4. Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998.
5. Jiayang Liu, Lin Zhong, Jehan Wickramasuriya, and Venu Vasudevan. uwave: Accelerometer-based personalized gesture recognition and its applications. Pervasive and Mobile Computing, 5(6):657-675, 2009.
6. Timo Pylvänäinen. Accelerometer based gesture recognition using continuous hmms. In Pattern Recognition and Image Analysis, pages 639-646. Springer, 2005.
7. Jeen-Shing Wang and Fang-Chen Chuang. An accelerometer-based digital pen with a trajectory recognition algorithm for handwritten digit and gesture recognition. Industrial Electronics, IEEE Transactions on, 59(7):2998-3007, 2012.
8. Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. Gesture recognition with a 3-d accelerometer. In Ubiquitous intelligence and computing, pages 25-38. Springer, 2009.