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Human Activity Recognition (HAR) using deep neural network has become a hot topic in human-computer interaction.
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Kwapisz, J.R.; Weiss, G.M.; Moore, S. Activity recognition using cell phone accelerometers. SIGKDD Explorations 2010, 12, 74-82. doi:10.1145/1964897.1964918
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2012
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Pascanu, R.; Mikolov, T.; Bengio, Y. On the difficulty of training recurrent neural networks. Proceedings of the 30th International Conference on Machine Learning, ICML 2013, Atlanta, GA, USA, 16-21 June 2013, 2013, pp. 1310-1318
2013
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Ordóñez, F.J.; Englebienne, G.; de Toledo, P.; van Kasteren, T.; Sanchis, A.; Kröse, B.J.A. In-Home Activity Recognition: Bayesian Inference for Hidden Markov Models. IEEE Pervasive Computing 2014, 13, 67-75. doi:10.1109/MPRV.2014.52
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Baños, O.; García, R.; Terriza, J.A.H.; Damas, M.; Pomares, H.; Ruiz, I.R.; Saez, A.; Villalonga, C. mHealthDroid: A Novel Framework for Agile Development of Mobile Health Applications. Ambient Assisted Living and Daily Activities - 6th International Work-Conference, IWAAL 2014, Belfast, UK, December 2-5, 2014. Proceedings, 2014, pp. 91-98. doi:10.1007/978-3-319-13105-4_14
2014
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Attal, F.; Mohammed, S.; Dedabrishvili, M.; Chamroukhi, F.; Oukhellou, L.; Amirat, Y. Physical Human Activity Recognition Using Wearable Sensors. Sensors 2015, 15, 31314-31338. doi:10.3390/s151229858
2015
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Yang, J.; Nguyen, M.N.; San, P.P.; Li, X.; Krishnaswamy, S. Deep Convolutional Neural Networks on Multichannel Time Series for Human Activity Recognition. Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25-31, 2015, 2015, pp. 3995-4001
2015
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Ronao, C.A.; Cho, S. Deep Convolutional Neural Networks for Human Activity Recognition with Smartphone Sensors. Neural Information Processing - 22nd International Conference, ICONIP 2015, Istanbul, Turkey, November 9-12, 2015, Proceedings, Part IV, 2015, pp. 46-53. doi:10.1007/978-3-319-26561-2_6
2015
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Ioffe, S.; Szegedy, C. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6-11 July 2015, 2015, pp. 448-456
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2016
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Kumar, A.; Irsoy, O.; 413 Ondruska, P.; Iyyer, M.; Bradbury, J.; Gulrajani, I.; Zhong, V.; Paulus, R.; Socher, R. Ask Me Anything: Dynamic Memory Networks for Natural Language Processing. Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016, 2016, pp. 1378-1387
2016
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Hammerla, N.Y.; Halloran, S.; Plötz, T. Deep, Convolutional, and Recurrent Models for Human Activity Recognition Using Wearables. Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, USA, 9-15 July 2016, 2016, pp. 1533-1540
2016
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Neverova, N.;Wolf, C.; Lacey, G.; Fridman, L.; Chandra, D.; Barbello, B.; Taylor, G.W. Learning Human Identity From Motion Patterns. IEEE Access 2016, 4, 1810-1820. doi:10.1109/ACCESS.2016.2557846
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Lisowska, A.; O’Neil, A.; Poole, I. Cross-cohort Evaluation of Machine Learning Approaches to Fall Detection from Accelerometer Data. Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 5: HEALTHINF, Funchal, Madeira, Portugal, January 19-21, 2018., 2018, pp. 77-82. doi:10.5220/0006554400770082
2018
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Sarkar, A.; Dasgupta, S.; Naskar, S.K.; Bandyopadhyay, S. Says Who? Deep Learning Models for Joint Speech Recognition, Segmentation and Diarization. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2018, Calgary, AB, Canada, April 15-20, 2018, 2018, pp. 5229-5233. doi:10.1109/ICASSP.2018.8462375
2018
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2016
Cited alongside, same era.
He, K.; Zhang, X.; Ren, S.; Sun, J. Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016, 2016, pp. 770-778. doi:10.1109/CVPR.2016.90
2016
Cited alongside, same era.
Morales, F.J.O.; Roggen, D. Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition. Sensors 2016, 16, 115. doi:10.3390/s16010115
2016
Cited alongside, same era.
Baldi, P.; Sadowski, P.J. A theory of local learning, the learning channel, and the optimality of backpropagation. Neural Networks 2016, 83, 51-74. doi:10.1016/j.neunet.2016.07.006
2016
Cited alongside, same era.
Feichtenhofer, C.; Pinz, A.; Wildes, R.P. Spatiotemporal Residual Networks for Video Action Recognition. Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, 2016, pp. 3468-3476
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
Yang, Z.; He, X.; Gao, J.; Deng, L.; Smola, A.J. Stacked Attention Networks for Image Question Answering. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27-30 June 2016; pp. 21-29, doi:10.1109/CVPR.2016.10
2016
Cited alongside, same era.
Xiao, Z.; Lim,H.B.; Ponnambalam, L. Participatory Sensing for Smart Cities: A Case Study on Transport Trip Quality Measurement. IEEE Trans. Industrial Informatics 2017, 13, 759-770. doi:10.1109/TII.2017.2678522
2017
Cited alongside, same era.
Shi, Z.; Zhang, J.A.; Xu, R.; Fang, G. Human Activity Recognition Using Deep Learning Networks with Enhanced Channel State Information. IEEE Globecom Workshops, GC Wkshps 2018, Abu Dhabi, United Arab Emirates, December 9-13, 2018, 2018, pp. 1-6. doi:10.1109/GLOCOMW.2018.8644435
2018
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Xu, W.; Pang, Y.; Yang, Y.; Liu, Y. Human Activity Recognition Based On Convolutional Neural Network. 24th International Conference on Pattern Recognition, ICPR 2018, Beijing, China, August 20-24, 2018, 2018, pp. 165-170. doi:10.1109/ICPR.2018.8545435
2018
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Yang, Z.; Raymond, O.I.; Zhang, C.; Wan, Y.; Long, J. DFTerNet: Towards 2-bit Dynamic Fusion Networks for Accurate Human Activity Recognition. IEEE Access 2018, 6, 56750-56764. doi:10.1109/ACCESS.2018.2873315
2018
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Ramamurthy, S.R.; Roy, N. Recent trends in machine learning for human activity recognition - A survey. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 2018, 8. doi:10.1002/widm.1254
2018
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Gu, F.; Khoshelham, K.; Valaee, S.; Shang, J.; Zhang, R. Locomotion Activity Recognition Using Stacked Denoising Autoencoders. IEEE Internet of Things Journal 2018, 5, 2085-2093. doi:10.1109/JIOT.2018.2823084
2018
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Milenkoski, M.; Trivodaliev, K.; Kalajdziski, S.; Jovanov, M.; Stojkoska, B.R. Real time human activity recognition on smartphones using LSTM networks. 41st International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2018, Opatija, Croatia, May 21-25, 2018, 2018, pp. 1126-1131. doi:10.23919/MIPRO.2018.8400205
2018
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Meng, B.; Liu, X.; Wang, X. Human action recognition based on quaternion spatial-temporal convolutional neural network and LSTM in RGB videos. Multimedia Tools Appl. 2018, 77, 26901-26918. doi:10.1007/s11042-018-5893-9
2018
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Wang, X.; Girshick, R.B.; Gupta, A.; He, K. Non-Local Neural Networks. 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018, 2018, pp. 7794-7803. doi:10.1109/CVPR.2018.00813
2018
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Li, F.; Shirahama, K.; Nisar, M.A.; Köping, L.; Grzegorzek, M. Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors. Sensors 2018, 18, 679. doi:10.3390/s18020679
2018
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2018
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Sukor, A.S.A.; Zakaria, A.; Rahim, N.A.; Kamarudin, L.M.; Setchi, R.; Nishizaki, H. A hybrid approach of knowledge-driven and data-driven reasoning for activity recognition in smart homes. Journal of Intelligent and Fuzzy Systems 2019, 36, 4177-4188. doi:10.3233/JIFS-169976
2019
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Yang, Z.; Raymond, O.I.; Sun, W.; Long, J. Deep Attention-Guided Hashing. IEEE Access 2019, 7, 11209-11221. doi:10.1109/ACCESS.2019.2891894
2019
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