Convolutional neural networks for human activity recognition using multiple accelerometer and gyroscope sensors

Proceedings of the International Joint Conference on Neural Networks

Volumn 2016-October, Issue , 2016, Pages 381-388

  Ha, Sojeong ^a   Choi, Seungjin ^a  

^a Pohang University of Science and Technology (POSTECH)   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

ACCELEROMETERS; BENCHMARKING; GYROSCOPES; MODAL ANALYSIS; NEURAL NETWORKS; PATTERN RECOGNITION; TIME SERIES;

BENCHMARK DATASETS; CONVOLUTIONAL NEURAL NETWORK; GYROSCOPE SENSORS; HUMAN ACTIVITY RECOGNITION; MULTIVARIATE TIME SERIES; TEMPORAL AND SPATIAL; TEMPORAL DIMENSIONS; UNIVARIATE TIME SERIES;

CONVOLUTION;

EID: 85007256354     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2016.7727224     Document Type: Conference Paper

References (24)

1. T. Chouhury, S. Consolvo, B. Harrison, J. Hightower, A. LaMarca, L. LeGrand, A. Rahimi, A. Rea, G. Borriello, B. Hemingway, P. Klasnja, K. Koscher, J. A. Landay, J. Lester, D. Wyatt, and D. Haehnel, "The mobile sensing platform: An embedded activity recognition system", IEEE Pervasive Computing, vol. 7, no. 2, pp. 32-41, 2008.
2. C. R. Wren and E. M. Tapia, "Toward scalable activity recognition for sensor networks", in Locationa and Context Awareness LNCS, vol. 3987. Springer, 2006, pp. 168-185.
3. K. Van Laerhoven and K. Aidoo, "Teaching context to applications", Personal and Ubiquitous Computing, vol. 5, pp. 46-49, 2001.
4. M. Stikic and B. Schiele, "Activity recognition from sparsely labeled data using multi-instance learning", in Location and Context Awareness LNCS, vol. 5561. Springer, 2009, pp. 156-173.
5. L. Bao and S. S. Intille, "Activity recognition from user-annotated acceleration data", in Pervasive Computing LNCS, vol. 3001. Springer, 2004, pp. 1-17.
6. T. Plötz, H. Y. Hammerla, and P. Olivier, "Feature learning for activity recognition in uniquitous computing", in Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), Barcelona, Spain, 2011.
7. A. Krause, D. P. Siewiorek, A. Smailagic, and J. Farringdon, "Unsupervised, dynamic identification of physiological and activity context in wearable computing", in Proceedings of the IEEE International Symposium on Wearable Computers (ISWC), 2003.
8. J. Yamato, J. Ohya, and K. Ishii, "Recognizing human action in timesequential images using hidden Markov model", in Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 1992.
9. H. Lu, J. Yang, Z. Liu, N. D. Lane, T. Choudhury, and A. T. Campbell, "The jigsaw continuous sensing engine for mobile phone applications", in Proceedings of the ACM Conference on Embedded Networked Sensor Systems (SenSys), Zurich, Switzerland, 2010.
10. S. B. Wang, A. Quattoni, L.-P. Morency, D. Demirdjian, and T. Darrell, "Hidden conditional random fields for gesture recognition", in Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), New York, NY, USA, 2006.
11. M. Zeng, L. T. Nguyen, B. Yu, O. J. Mengshoel, and J. Zhan, "Convolutional neural networks for human activity recognition using mobile sensors", in Proceedings of the Sixth International Conference on Mobile Computing, Applications and Services (MobiCASE 2014), Austin, Texas, USA, 2014.
12. S. Duffner, S. Berlemont, G. Lefebvre, and C. Garcia, "3D gesture classification with convolutional neural networks", in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Florence, Italy, 2014.
13. J. B. Yang, M. N. Nguyen, P. P. San, X. L. Li, and S. Krishnaswamy, "Deep convolutional neural networks on multichannel time series for human activity recognition", in Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), Buenos Aires, Argentina, 2015.
14. S. Ha, J.-M. Yun, and S. Choi, "Multi-modal convolutional neural networks for activity recognition", in Proceedings of IEEE International Conference on Systems, Man, and Cybernetics (SMC), Hong Kong, 2015.
15. O. Banos, R. Garcia, J. A. Holgado-Terriza, M. Damas, H. Pomares, I. Rojas, A. Saez, and C. Villalonga, "mHealthDroid: A novel framework for agile development of mobile health applications", in Proceedings of the International Work-Conference on Ambient Assisted Living (IWAAL), 2014.
16. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition", Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, 1998.
17. O. Abdel-Hamid, A.-R. Mohamed, H. Jiang, and G. Penn, "Applying convolutional neural networks concepts to hybrid NN-HMM model for speech recognition", in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Kyoto, Japan, 2012.
18. V. Nair and G. E. Hinton, "Rectified linear units improve restricted Boltzmann machines", in Proceedings of the International Conference on Machine Learning (ICML), Haifa, Israel, 2010.
19. H. Lee and S. Choi, "Group nonnegative matrix factorization for EEG classification", in Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), Clearwater Beach, Florida, 2009.
20. M. Salzmann, C. H. Ek, R. Urtasun, and T. Darrell, "Factorized orthogonal latent spaces", in Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), Sardinia, Italy, 2010.
21. Y. Kang and S. Choi, "Restricted deep belief networks for multi-view learning", in Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), Athens, Greece, 2011.
22. G. E. Dahl, T. N. Sainath, and G. E. Hinton, "Improving deep neural networks for LVCSR using rectified linear units and dropout", in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, Canada, 2013.
23. K. Murphy, "Hidden Markov model (HMM) toolbox for Matlab", 1998, available at http://www.ai.mit.edu/~murphyk/Software/HMM/hmm.html.
24. R. E. Fan, K. W. Chang, C. J. Hsieh, X. R. Wang, and C. J. Lin, "LIBLINEAR: A library for large linear classification", Journal of Machine Learning Research, vol. 9, pp. 1871-1874, 2008.