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Human activity recognition (HAR) is an important research field in ubiquitous computing where the acquisition of large-scale labeled sensor data is tedious, labor-intensive and time consuming.
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
1998
Earlier work this paper cites.
L. v. d. Maaten and G. Hinton, “Visualizing data using t-sne,” in Journal of Machine Learning Research , 2008
2008
Earlier work this paper cites.
M. Gövercin, Y. Költzsch, M. Meis, S. Wegel, M. Gietzelt, J. Spehr, S. Winkelbach, M. Marschollek, and E. Steinhagen-Thiessen, “Defining the user requirements for wearable and optical fall prediction and fall detection devices for home use,” Informatics for Health and Social Care , vol. 35, no. 3-4, pp. 177–187, 2010
2010
Earlier work this paper cites.
J. Frank, S. Mannor, and D. Precup, “Activity and gait recognition with time-delay embeddings,” in AAAI Conference on Artificial Intelligence (AAAI) , 2010
2010
Earlier work this paper cites.
T. Plötz, N. Y. Hammerla, and P. L. Olivier, “Feature learning for activity recognition in ubiquitous computing,” in International Joint Conference on Artificial Intelligence (IJCAI) , 2011
2011
Earlier work this paper cites.
M. Zhang and A. A. Sawchuk, “Usc-had: a daily activity dataset for ubiquitous activity recognition using wearable sensors,” in Proceedings of the ACM Conference on Ubiquitous Computing (UbiComp) , 2012, pp. 1036–1043
2012
Earlier work this paper cites.
D. Anguita, A. Ghio, L. Oneto, X. Parra, and J. L. Reyes-Ortiz, “A public domain dataset for human activity recognition using smartphones.” in European Symposium on Artificial Neural Networks (ESANN) , 2013
2013
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Conference on Neural Information Processing Systems (NIPS) , 2014, pp. 2672–2680
2014
Earlier work this paper cites.
N. Y. Hammerla, J. Fisher, P. Andras, L. Rochester, R. Walker, and T. Plötz, “PD disease state assessment in naturalistic environments using deep learning,” in AAAI Conference on Artificial Intelligence (AAAI) , 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in The International Conference on Learning Representations (ICLR) , 2015
2015
Earlier work this paper cites.
E. Denton, S. Chintala, A. Szlam, and R. Fergus, “Deep generative image models using a laplacian pyramid of adversarial networks,” in Conference on Neural Information Processing Systems (NIPS) , 2015, p. 1486–1494
2015
Earlier work this paper cites.
G. Perarnau, J. van de Weijer, B. Raducanu, and J. M. Álvarez, “Invertible Conditional GANs for image editing,” in NIPS Workshop on Adversarial Training , 2016
2016
Earlier work this paper cites.
X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel, “InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets,” in Conference on Neural Information Processing Systems (NIPS) , 2016, p. 2180–2188
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, “Improved techniques for training gans,” in Conference on Neural Information Processing Systems (NIPS) , 2016, pp. 2234–2242
2016
Cited alongside, same era.
R. Zhang, P. Isola, and A. A. Efros, “Colorful image colorization,” in European Conference on Computer Vision (ECCV) , 2016, pp. 649–666
2016
Cited alongside, same era.
M. A. Alsheikh, A. Selim, D. Niyato, L. Doyle, S. Lin, and H.-P. Tan, “Deep activity recognition models with triaxial accelerometers,” in Workshops at the AAAI Conference on Artificial Intelligence , 2016
2016
Cited alongside, same era.
J. Wang, X. Zhang, Q. Gao, H. Yue, and H. Wang, “Device-free wireless localization and activity recognition: A deep learning approach,” IEEE Trans. Veh. Technol. , vol. 66, no. 7, pp. 6258–6267, 2016
2016
Cited alongside, same era.
T. Karras, T. Aila, S. Laine, and J. Lehtinen, “Progressive growing of gans for improved quality, stability, and variation,” The International Conference on Learning Representations (ICLR) , 2018
2018
Later among the works it cites.
M. Freitag, S. Amiriparian, S. Pugachevskiy, N. Cummins, and B. Schuller, “audeep: Unsupervised learning of representations from audio with deep recurrent neural networks,” Journal of Machine Learning Research , vol. 18, no. 173, pp. 1–5, 2018
2018
Later among the works it cites.
A. A. Varamin, E. Abbasnejad, Q. Shi, D. C. Ranasinghe, and H. Rezatofighi, “Deep auto-set: A deep auto-encoder-set network for activity recognition using wearables,” in Proceedings of the EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous) , 2018, p. 246–253
2018
Later among the works it cites.
A. Jayatilaka, Q. H. Dang, S. J. Chen, R. Visvanathan, C. Fumeaux, and D. C. Ranasinghe, “Designing batteryless wearables for hospitalized older people,” in Proceedings of the International Symposium on Wearable Computers (ISWC) , 2019, p. 91–95
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A. Mannini, M. Rosenberger, W. L. Haskell, A. M. Sabatini, and S. S. Intille, “Activity recognition in youth using single accelerometer placed at wrist or ankle,” Medicine and Science in Sports and Exercise , vol. 49, no. 4, p. 801, 2017
2017
Cited alongside, same era.
R. L. Shinmoto Torres, R. Visvanathan, D. Abbott, K. D. Hill, and D. C. Ranasinghe, “A battery-less and wireless wearable sensor system for identifying bed and chair exits in a pilot trial in hospitalized older people,” PLOS ONE , vol. 12, no. 10, pp. 1–25, 10 2017
2017
Cited alongside, same era.
J. Donahue, P. Krähenbühl, and T. Darrell, “Adversarial feature learning,” in The International Conference on Learning Representations (ICLR) , 2017
2017
Cited alongside, same era.
V. Dumoulin, I. Belghazi, B. Poole, O. Mastropietro, A. Lamb, M. Arjovsky, and A. Courville, “Adversarially learned inference,” in The International Conference on Learning Representations (ICLR) , 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
M. Alzantot, S. Chakraborty, and M. B. Srivastava, “SenseGen: A deep learning architecture for synthetic sensor data generation,” in IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) , 2017
2017
Cited alongside, same era.
T. Che, Y. Li, A. P. Jacob, Y. Bengio, and W. Li, “Mode regularized generative adversarial networks,” in The International Conference on Learning Representations (ICLR) , 2017
2017
Cited alongside, same era.
B. Almaslukh, J. AlMuhtadi, and A. Artoli, “An effective deep autoencoder approach for online smartphone-based human activity recognition,” Int. J. Comput. Sci. Netw. Secur , vol. 17, no. 4, pp. 160–165, 2017
2017
Cited alongside, same era.
2019
Later among the works it cites.
M. Chesser, A. Jayatilaka, R. Visvanathan, C. Fumeaux, A. Sample, and D. C. Ranasinghe, “Super low resolution RF powered accelerometers for alerting on hospitalized patient bed exits,” in IEEE International Conference on Pervasive Computing and Communications (PerCom) , 2019, pp. 1–10
2019
Later among the works it cites.
H. Haresamudram, D. V. Anderson, and T. Plötz, “On the role of features in human activity recognition,” in Proceedings of the International Symposium on Wearable Computers (ISWC) , 2019, pp. 78–88
2019
Later among the works it cites.
L. Bai, C. Yeung, C. Efstratiou, and M. Chikomo, “Motion2Vector: Unsupervised learning in human activity recognition using wrist-sensing data,” in Proceedings of the ACM International Symposium on Wearable Computers (ISWC) , 2019, p. 537–542
2019
Later among the works it cites.
J. Donahue and K. Simonyan, “Large scale adversarial representation learning,” in Conference on Neural Information Processing Systems (NIPS) , 2019, pp. 10 542–10 552
2019
Later among the works it cites.
J. Yoon, D. Jarrett, and M. van der Schaar, “Time-series generative adversarial networks,” in Conference on Neural Information Processing Systems (NIPS) , 2019, pp. 5508–5518
2019
Later among the works it cites.
A. Brock, J. Donahue, and K. Simonyan, “Large scale GAN training for high fidelity natural image synthesis,” in The International Conference on Learning Representations (ICLR) , 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
A. Fellger, G. Sprint, D. Weeks, E. Crooks, and D. J. Cook, “Wearable device-independent next day activity and next night sleep prediction for rehabilitation populations,” IEEE J. Transl. Eng. Health Med. , vol. 8, pp. 1–9, 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
H. Haresamudram, A. Beedu, V. Agrawal, P. L. Grady, I. Essa, J. Hoffman, and T. Plötz, “Masked reconstruction based self-supervision for human activity recognition,” in Proceedings of the International Symposium on Wearable Computers (ISWC) , 2020, p. 45–49
2020
Later among the works it cites.
C. I. Tang, I. Perez-Pozuelo, D. Spathis, S. Brage, N. Wareham, and C. Mascolo, “SelfHAR: Improving human activity recognition through self-training with unlabeled data,” Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. , vol. 5, no. 1, 2021
2021
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