Deep unsupervised methods towards behavior analysis in ubiquitous sensor data
Tài liệu tham khảo
Agrawal, P., Carreira, J., Malik, J.: Learning to see by moving. In: Proceedings of the IEEE International Conference On Computer Vision, pp. 37–45 (2015).
Ali, 2015, Travel behavior analysis using smart card data, KSCE J. Civil Eng., 20
Arandjelovic, R., Zisserman, A.: Objects that sound. In: Proceedings of the European Conference On Computer Vision (ECCV), pp. 435–451 (2018).
Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.0127 (2018).
Bengio, 2013, Representation learning: a review and new perspectives, IEEE Trans. Pattern Anal. Mach. Intellig., 35, 1798, 10.1109/TPAMI.2013.50
Blei, 2003, Latent dirichlet allocation, J. Mach. Learn. Res., 3, 993
Borazio, 2014, Towards benchmarked sleep detection with wrist-worn sensing units, 125
Bourobou, 2015, User activity recognition in smart homes using pattern clustering applied to temporal ANN algorithm, Sensors, 15, 11953, 10.3390/s150511953
Castanedo, 2014, Learning routines over long-term sensor data using topic models, Expert Syst., 31, 365, 10.1111/exsy.12033
Chen, 2009, Collaborative filtering for orkut com-munities: discovery of user latent behavior, 681
Cook, 2009, Assessing the quality of activities in a smart environment, Methods Inf. Med., 48, 480, 10.3414/ME0592
Deerwester, 1990, Indexing by latent semantic analysis, J. Am. Soc. Inf. Sci., 41, 391, 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9
Doersch, C., Gupta, A., Efros, A.A.: Unsupervised visual representation learning by context prediction. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1422–1430 (2015).
Doersch, C., Zisserman, A.: Multi-task self-supervised visual learning. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2051–2060 (2017).
Erhan, 2010, Why does unsupervised pre-training help deep learning?, J. Mach. Learn. Res., 11, 625
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, KDD’96, p. 226–231. AAAI Press (1996).
Fernando, B., Bilen, H., Gavves, E., Gould, S.: Self-supervised video representation learning with odd-one-out networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3636–3645 (2017).
Ferrari, A., Micucci, D., Mobilio, M., Napoletano, P.: Trends in human activity recognition using smart-phones. J. Reliab. Intell. Environ. pp. 1–25 (2021).
Figo, 2010, Preprocessing techniques for context recognition from accelerometer data, Pers. Ubiquitous Comput., 14, 645, 10.1007/s00779-010-0293-9
Gaujoux, 2010, A flexible r package for nonnegative matrix factorization, BMC Bioinform., 11, 367, 10.1186/1471-2105-11-367
Gomez, L., Patel, Y., Rusi∼nol, M., Karatzas, D., Jawahar, C.: Self-supervised learning of visual features through embedding images into text topic spaces. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4230–4239 (2017).
Hammerla, N.Y., Halloran, S., Plotz, T.: Deep, convolutional, and recurrent models for human activity recognition using wearables. arXiv preprint arXiv:1604.08880 (2016).
Hofmann, 1999, Probabilistic latent semantic analysis, 289
Huynh, T., Fritz, M., Schiele, B.: Discovery of activity patterns using topic models. In: UbiComp, vol. 8, pp. 10–19 (2008).
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).
Lakoju, 2021, Unsupervised learning for product use activity recognition: an exploratory study of a "chatty device", Sensors, 21, 10.3390/s21154991
Lloyd, 1982, Least squares quantization in PCM, IEEE Trans. Inf. Theory, 28, 129, 10.1109/TIT.1982.1056489
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013).
Morales, F.J.O., Roggen, D.: Deep convolutional feature transfer across mobile activity recognition do-mains, sensor modalities and locations. In: Proceedings of the 2016 ACM International Symposium on Wearable Computers, pp. 92–99 (2016).
Nguyen, 2021, Trends in human activity recognition with focus on machine learning and power requirements, Mach. Learn. Appl., 5
Rashidi, 2010, Discovering activities to recognize and track in a smart environment, IEEE Trans. Knowl. Data Eng., 23, 527, 10.1109/TKDE.2010.148
Saeed, 2019, Multi-task self-supervised learning for human activity detection, Proc. ACM Interact., Mob., Wearable and Ubiquitous Technol., 3, 1, 10.1145/3328932
Wang, 2019, Deep learning for sensor-based activity recognition: a survey, Pattern Recognit. Lett., 119, 3, 10.1016/j.patrec.2018.02.010
Wren, 2007, The MERL motion detector dataset: 2007 workshop on massive datasets, Tech. Rep.,, 3
Yang, J., Nguyen, M.N., San, P.P., Li, X.L., Krish-naswamy, S.: Deep convolutional neural networks on multichannel time series for human activity recognition. In: Twenty-Fourth International Joint Conference on Artficial Intelligence (2015).