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Recently, deep learning has represented an important research trend in human activity recognition (HAR).
2006
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J. R. Kwapisz, G. M. Weiss, and S. A. Moore, “Activity recognition using cell phone accelerometers,” ACM SigKDD Explorations Newsletter , vol. 12, no. 2, pp. 74–82, 2011
2011
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X. Glorot, A. Bordes, and Y. Bengio, “Deep sparse rectifier neural networks,” in Proceedings of the fourteenth international conference on artificial intelligence and statistics , 2011, pp. 315–323
2011
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O. D. Lara and M. A. Labrador, “A survey on human activity recognition using wearable sensors,” IEEE communications surveys & tutorials , vol. 15, no. 3, pp. 1192–1209, 2012
2012
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D. Anguita, A. Ghio, L. Oneto, X. Parra, and J. L. Reyes-Ortiz, “Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine,” in International workshop on ambient assisted living . Springer, 2012, pp. 216–223
2012
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A. Reiss and D. Stricker, “Introducing a new benchmarked dataset for activity monitoring,” in 2012 16th International Symposium on Wearable Computers . IEEE, 2012, pp. 108–109
2012
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2013
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R. Chavarriaga, H. Sagha, A. Calatroni, S. T. Digumarti, G. Tröster, J. d. R. Millán, and D. Roggen, “The opportunity challenge: A benchmark database for on-body sensor-based activity recognition,” Pattern Recognition Letters , vol. 34, no. 15, pp. 2033–2042, 2013
2013
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A. Bulling, U. Blanke, and B. Schiele, “A tutorial on human activity recognition using body-worn inertial sensors,” ACM Computing Surveys (CSUR) , vol. 46, no. 3, pp. 1–33, 2014
2014
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2014
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O. Banos, J.-M. Galvez, M. Damas, H. Pomares, and I. Rojas, “Window size impact in human activity recognition,” Sensors , vol. 14, no. 4, pp. 6474–6499, 2014
2014
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2014
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M. Zeng, L. T. Nguyen, B. Yu, O. J. Mengshoel, J. Zhu, P. Wu, and J. Zhang, “Convolutional neural networks for human activity recognition using mobile sensors,” in 6th International Conference on Mobile Computing, Applications and Services . IEEE, 2014, pp. 197–205
2014
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2014
Cited alongside, same era.
J. 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 Twenty-Fourth International Joint Conference on Artificial Intelligence , 2015
2015
Cited alongside, same era.
2015
Cited alongside, same era.
F. Ordóñez and D. Roggen, “Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition,” Sensors , vol. 16, p. 115, 01 2016
2016
Cited alongside, same era.
R. Teja Mullapudi, W. R. Mark, N. Shazeer, and K. Fatahalian, “Hydranets: Specialized dynamic architectures for efficient inference,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 8080–8089
2018
Later among the works it cites.
Z. Yang, O. I. Raymond, C. Zhang, Y. Wan, and J. Long, “Dfternet: Towards 2-bit dynamic fusion networks for accurate human activity recognition,” IEEE Access , vol. 6, pp. 56 750–56 764, 2018
2018
Later among the works it cites.
M. Zeng, H. Gao, T. Yu, O. J. Mengshoel, H. Langseth, I. Lane, and X. Liu, “Understanding and improving recurrent networks for human activity recognition by continuous attention,” in Proceedings of the 2018 ACM International Symposium on Wearable Computers , 2018, pp. 56–63
2018
Later among the works it cites.
F. Li, K. Shirahama, M. A. Nisar, L. Köping, and M. Grzegorzek, “Comparison of feature learning methods for human activity recognition using wearable sensors,” Sensors , vol. 18, no. 2, p. 679, 2018
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D. Ravi, C. Wong, B. Lo, and G.-Z. Yang, “Deep learning for human activity recognition: A resource efficient implementation on low-power devices,” in 2016 IEEE 13th international conference on wearable and implantable body sensor networks (BSN) . IEEE, 2016, pp. 71–76
2016
Cited alongside, same era.
A. Khan, N. Hammerla, S. Mellor, and T. Plötz, “Optimising sampling rates for accelerometer-based human activity recognition,” Pattern Recognition Letters , vol. 73, pp. 33–40, 2016
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2017
Cited alongside, same era.
D. Micucci, M. Mobilio, and P. Napoletano, “Unimib shar: A dataset for human activity recognition using acceleration data from smartphones,” Applied Sciences , vol. 7, no. 10, p. 1101, 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
A. Ignatov, “Real-time human activity recognition from accelerometer data using convolutional neural networks,” Applied Soft Computing , vol. 62, pp. 915–922, 2018
2018
Cited alongside, same era.
Z. Wu, T. Nagarajan, A. Kumar, S. Rennie, L. S. Davis, K. Grauman, and R. Feris, “Blockdrop: Dynamic inference paths in residual networks,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2018
2018
Cited alongside, same era.
2018
Later among the works it cites.
J. Wang, Y. Chen, S. Hao, X. Peng, and L. Hu, “Deep learning for sensor-based activity recognition: A survey,” Pattern Recognition Letters , vol. 119, pp. 3–11, 2019
2019
Later among the works it cites.
B. Yang, G. Bender, Q. V. Le, and J. Ngiam, “Condconv: Conditionally parameterized convolutions for efficient inference,” in Advances in Neural Information Processing Systems , 2019, pp. 1305–1316
2019
Later among the works it cites.
K. Wang, J. He, and L. Zhang, “Attention-based convolutional neural network for weakly labeled human activities’ recognition with wearable sensors,” IEEE Sensors Journal , vol. 19, no. 17, pp. 7598–7604, 2019
2019
Later among the works it cites.
X. Li, S. Chen, X. Hu, and J. Yang, “Understanding the disharmony between dropout and batch normalization by variance shift,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 2682–2690
2019
Later among the works it cites.
2019
Later among the works it cites.
2020
Closest in time.
2020
Closest in time.
Q. Teng, K. Wang, L. Zhang, and J. He, “The layer-wise training convolutional neural networks using local loss for sensor based human activity recognition,” IEEE Sensors Journal , vol. PP, pp. 1–1, 03 2020
2020
Closest in time.