{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T16:53:20Z","timestamp":1784998400019,"version":"3.55.0"},"reference-count":264,"publisher":"Association for Computing Machinery (ACM)","issue":"8","license":[{"start":{"date-parts":[[2021,10,4]],"date-time":"2021-10-04T00:00:00Z","timestamp":1633305600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["42174050 and 42171427"],"award-info":[{"award-number":["42174050 and 42171427"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Key Research and Development Program of China","award":["2016YFB0502200"],"award-info":[{"award-number":["2016YFB0502200"]}]},{"DOI":"10.13039\/501100021171","name":"Guangdong Basic and Applied Basic Research Foundation","doi-asserted-by":"crossref","award":["2019A1515011910"],"award-info":[{"award-number":["2019A1515011910"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Shenzhen Scientific Research and Development Funding Program","award":["JCYJ20190808113603556"],"award-info":[{"award-number":["JCYJ20190808113603556"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"published-print":{"date-parts":[[2022,11,30]]},"abstract":"<jats:p>Human activity recognition is a key to a lot of applications such as healthcare and smart home. In this study, we provide a comprehensive survey on recent advances and challenges in human activity recognition (HAR) with deep learning. Although there are many surveys on HAR, they focused mainly on the taxonomy of HAR and reviewed the state-of-the-art HAR systems implemented with conventional machine learning methods. Recently, several works have also been done on reviewing studies that use deep models for HAR, whereas these works cover few deep models and their variants. There is still a need for a comprehensive and in-depth survey on HAR with recently developed deep learning methods.<\/jats:p>","DOI":"10.1145\/3472290","type":"journal-article","created":{"date-parts":[[2021,10,5]],"date-time":"2021-10-05T00:42:42Z","timestamp":1633394562000},"page":"1-34","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":222,"title":["A Survey on Deep Learning for Human Activity Recognition"],"prefix":"10.1145","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3408-982X","authenticated-orcid":false,"given":"Fuqiang","family":"Gu","sequence":"first","affiliation":[{"name":"Chongqing University, Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mu-Huan","family":"Chung","sequence":"additional","affiliation":[{"name":"University of Toronto, Toronto, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark","family":"Chignell","sequence":"additional","affiliation":[{"name":"University of Toronto, Toronto, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shahrokh","family":"Valaee","sequence":"additional","affiliation":[{"name":"University of Toronto, Toronto, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baoding","family":"Zhou","sequence":"additional","affiliation":[{"name":"Shenzhen University, Shenzhen, Guangdong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xue","family":"Liu","sequence":"additional","affiliation":[{"name":"McGill University, Montreal, Quebec, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,10,4]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Mart\u00edn Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. Corrado Andy Davis Jeffrey Dean Matthieu Devin Sanjay Ghemawat Ian Goodfellow Andrew Harp Geoffrey Irving Michael Isard Yangqing Jia Rafal Jozefowicz Lukasz Kaiser Manjunath Kudlur Josh Levenberg Dandelion Man\u00e9 Rajat Monga Sherry Moore Derek Murray Chris Olah Mike Schuster Jonathon Shlens Benoit Steiner Ilya Sutskever Kunal Talwar Paul Tucker Vincent Vanhoucke Vijay Vasudevan Fernanda Vi\u00e9gas Oriol Vinyals Pete Warden Martin Wattenberg Martin Wicke Yuan Yu and Xiaoqiang Zheng. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Retrieved from https:\/\/www.tensorflow.org\/.  Mart\u00edn Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. Corrado Andy Davis Jeffrey Dean Matthieu Devin Sanjay Ghemawat Ian Goodfellow Andrew Harp Geoffrey Irving Michael Isard Yangqing Jia Rafal Jozefowicz Lukasz Kaiser Manjunath Kudlur Josh Levenberg Dandelion Man\u00e9 Rajat Monga Sherry Moore Derek Murray Chris Olah Mike Schuster Jonathon Shlens Benoit Steiner Ilya Sutskever Kunal Talwar Paul Tucker Vincent Vanhoucke Vijay Vasudevan Fernanda Vi\u00e9gas Oriol Vinyals Pete Warden Martin Wattenberg Martin Wicke Yuan Yu and Xiaoqiang Zheng. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Retrieved from https:\/\/www.tensorflow.org\/."},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the 2015 IEEE Conference on Computer Communications (INFOCOM\u201915)","author":"Abdelnasser Heba","unstructured":"Heba Abdelnasser , Moustafa Youssef , and Khaled A. Harras . 2015. Wigest: A ubiquitous wifi-based gesture recognition system . In Proceedings of the 2015 IEEE Conference on Computer Communications (INFOCOM\u201915) . IEEE, 1472\u20131480. Heba Abdelnasser, Moustafa Youssef, and Khaled A. Harras. 2015. Wigest: A ubiquitous wifi-based gesture recognition system. In Proceedings of the 2015 IEEE Conference on Computer Communications (INFOCOM\u201915). IEEE, 1472\u20131480."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1922649.1922653"},{"key":"e_1_2_1_4_1","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops .Women in Computer Vision (WiCV\u201917)","author":"Ahsan Unaiza","year":"2017","unstructured":"Unaiza Ahsan , Chen Sun , and Irfan Essa . 2017 . DiscrimNet: Semi-supervised action recognition from videos using generative adversarial networks . In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops .Women in Computer Vision (WiCV\u201917) . Unaiza Ahsan, Chen Sun, and Irfan Essa. 2017. DiscrimNet: Semi-supervised action recognition from videos using generative adversarial networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops .Women in Computer Vision (WiCV\u201917)."},{"key":"e_1_2_1_5_1","first-page":"160","article-title":"An effective deep autoencoder approach for online smartphone-based human activity recognition","volume":"17","author":"Almaslukh Bandar","year":"2017","unstructured":"Bandar Almaslukh , Jalal AlMuhtadi , and Abdelmonim Artoli . 2017 . An effective deep autoencoder approach for online smartphone-based human activity recognition . Int. J. Comput. Sci. Netw. Secur. 17 , 4 (2017), 160 \u2013 165 . Bandar Almaslukh, Jalal AlMuhtadi, and Abdelmonim Artoli. 2017. An effective deep autoencoder approach for online smartphone-based human activity recognition. Int. J. Comput. Sci. Netw. Secur. 17, 4 (2017), 160\u2013165.","journal-title":"Int. J. Comput. Sci. Netw. Secur."},{"key":"e_1_2_1_6_1","volume-title":"Proceedings of the Workshops at the 30th AAAI Conference on Artificial Intelligence.","author":"Alsheikh Mohammad Abu","year":"2016","unstructured":"Mohammad Abu Alsheikh , Ahmed Selim , Dusit Niyato , Linda Doyle , Shaowei Lin , and Hwee-Pink Tan . 2016 . Deep activity recognition models with triaxial accelerometers . In Proceedings of the Workshops at the 30th AAAI Conference on Artificial Intelligence. Mohammad Abu Alsheikh, Ahmed Selim, Dusit Niyato, Linda Doyle, Shaowei Lin, and Hwee-Pink Tan. 2016. Deep activity recognition models with triaxial accelerometers. In Proceedings of the Workshops at the 30th AAAI Conference on Artificial Intelligence."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2010.04.019"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.781"},{"key":"e_1_2_1_9_1","volume-title":"Proceedings of the 5th International Semantic Web Conference (ISWC\u201906)","volume":"1","author":"Anderson Ian","year":"2006","unstructured":"Ian Anderson and Henk Muller . 2006 . Practical activity recognition using gsm data . In Proceedings of the 5th International Semantic Web Conference (ISWC\u201906) , Vol. 1 . Ian Anderson and Henk Muller. 2006. Practical activity recognition using gsm data. In Proceedings of the 5th International Semantic Web Conference (ISWC\u201906), Vol. 1."},{"key":"e_1_2_1_10_1","volume-title":"Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN\u201913)","author":"Anguita Davide","year":"2013","unstructured":"Davide Anguita , Alessandro Ghio , Luca Oneto , Xavier Parra , and Jorge Luis Reyes-Ortiz . 2013 . A public domain dataset for human activity recognition using smartphones . In Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN\u201913) . Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, and Jorge Luis Reyes-Ortiz. 2013. A public domain dataset for human activity recognition using smartphones. In Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN\u201913)."},{"key":"e_1_2_1_11_1","first-page":"1172","article-title":"Does median filtering truly preserve edges better than linear filtering?Ann","volume":"37","author":"Arias-Castro Ery","year":"2009","unstructured":"Ery Arias-Castro , David L. Donoho , et\u00a0al. 2009 . Does median filtering truly preserve edges better than linear filtering?Ann . Stat. 37 , 3 (2009), 1172 \u2013 1206 . Ery Arias-Castro, David L. Donoho, et\u00a0al. 2009. Does median filtering truly preserve edges better than linear filtering?Ann. Stat. 37, 3 (2009), 1172\u20131206.","journal-title":"Stat."},{"key":"e_1_2_1_12_1","unstructured":"Martin Arjovsky Soumith Chintala and L\u00e9on Bottou. 2017. Wasserstein gan. arXiv:1701.07875. Retrieved from https:\/\/arxiv.org\/abs\/1701.07875.  Martin Arjovsky Soumith Chintala and L\u00e9on Bottou. 2017. Wasserstein gan. arXiv:1701.07875. Retrieved from https:\/\/arxiv.org\/abs\/1701.07875."},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.5555\/3045390.3045509"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITB.2009.2036165"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISWC.2009.14"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-13105-4_14"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.3390\/s140609995"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3097997"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1561\/2200000006"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6639349"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.50"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.25080\/Majora-92bf1922-003"},{"key":"e_1_2_1_23_1","volume-title":"Began: Boundary equilibrium generative adversarial networks. arXiv:1703.10717.","author":"Berthelot David","year":"2017","unstructured":"David Berthelot , Thomas Schumm , and Luke Metz . 2017 . Began: Boundary equilibrium generative adversarial networks. arXiv:1703.10717. Retrieved from https:\/\/arxiv.org\/abs\/1703.10717. David Berthelot, Thomas Schumm, and Luke Metz. 2017. Began: Boundary equilibrium generative adversarial networks. arXiv:1703.10717. Retrieved from https:\/\/arxiv.org\/abs\/1703.10717."},{"key":"e_1_2_1_24_1","volume-title":"Proceedings of the IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops\u201916)","author":"Bhattacharya Sourav","unstructured":"Sourav Bhattacharya and Nicholas D. Lane . 2016. From smart to deep: Robust activity recognition on smartwatches using deep learning . In Proceedings of the IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops\u201916) . IEEE, 1\u20136. Sourav Bhattacharya and Nicholas D. Lane. 2016. From smart to deep: Robust activity recognition on smartwatches using deep learning. In Proceedings of the IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops\u201916). IEEE, 1\u20136."},{"key":"e_1_2_1_25_1","volume-title":"Bracewell","author":"Bracewell Ronald Newbold","year":"1986","unstructured":"Ronald Newbold Bracewell and Ronald N . Bracewell . 1986 . The Fourier Transform and its Applications. Vol. 31999 . McGraw\u2013Hill New York . Ronald Newbold Bracewell and Ronald N. Bracewell. 1986. The Fourier Transform and its Applications. Vol. 31999. McGraw\u2013Hill New York."},{"key":"e_1_2_1_26_1","unstructured":"Jason Brownlee. 2019. How to Evaluate the Skill of Deep Learning Models. Retrieved from https:\/\/machinelearningmastery.com\/evaluate-skill-deep-learning-models\/.  Jason Brownlee. 2019. How to Evaluate the Skill of Deep Learning Models. Retrieved from https:\/\/machinelearningmastery.com\/evaluate-skill-deep-learning-models\/."},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2499621"},{"key":"e_1_2_1_28_1","volume-title":"A Tutorial on Human Activity Recognition Using Body-worn Inertial Sensors. Comput. Surv. 46, 3","author":"Bulling Andreas","year":"2014","unstructured":"Andreas Bulling , Ulf Blanke , and Bernt Schiele . 2014. A Tutorial on Human Activity Recognition Using Body-worn Inertial Sensors. Comput. Surv. 46, 3 ( 2014 ), 33:1\u201333:33. https:\/\/doi.org\/10.1145\/2499621 10.1145\/2499621 Andreas Bulling, Ulf Blanke, and Bernt Schiele. 2014. A Tutorial on Human Activity Recognition Using Body-worn Inertial Sensors. Comput. Surv. 46, 3 (2014), 33:1\u201333:33. https:\/\/doi.org\/10.1145\/2499621"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298698"},{"key":"e_1_2_1_30_1","volume-title":"Seven Theories of Human Society","author":"Campbell Tom","unstructured":"Tom Campbell . 1981. Seven Theories of Human Society . Clarendon Press , 169\u2013229. Tom Campbell. 1981. Seven Theories of Human Society. Clarendon Press, 169\u2013229."},{"key":"e_1_2_1_31_1","unstructured":"Joao Carreira Eric Noland Chloe Hillier and Andrew Zisserman. 2019. A short note on the kinetics-700 human action dataset. arXiv:1907.06987. Retrieved from https:\/\/arxiv.org\/abs\/1907.06987.  Joao Carreira Eric Noland Chloe Hillier and Andrew Zisserman. 2019. A short note on the kinetics-700 human action dataset. arXiv:1907.06987. Retrieved from https:\/\/arxiv.org\/abs\/1907.06987."},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2017.06.110"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.5555\/3367032.3367223"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCC.2012.2198883"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3381012"},{"key":"e_1_2_1_36_1","volume-title":"Workshop on Machine Learning Systems.","author":"Chen Tianqi","year":"2015","unstructured":"Tianqi Chen , Mu Li , Yutian Li , Min Lin , Naiyan Wang , Minjie Wang , Tianjun Xiao , Bing Xu , Chiyuan Zhang , and Zheng Zhang . 2015 . Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems. In Neural Information Processing Systems , Workshop on Machine Learning Systems. Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang. 2015. Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems. In Neural Information Processing Systems, Workshop on Machine Learning Systems."},{"key":"e_1_2_1_37_1","volume-title":"Infogan: Interpretable representation learning by information maximizing generative adversarial nets. In Advances in Neural Information Processing Systems. 2172\u20132180.","author":"Chen Xi","year":"2016","unstructured":"Xi Chen , Yan Duan , Rein Houthooft , John Schulman , Ilya Sutskever , and Pieter Abbeel . 2016 . Infogan: Interpretable representation learning by information maximizing generative adversarial nets. In Advances in Neural Information Processing Systems. 2172\u20132180. Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel. 2016. Infogan: Interpretable representation learning by information maximizing generative adversarial nets. In Advances in Neural Information Processing Systems. 2172\u20132180."},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01246-5_17"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2766780"},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/W14-4012"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1179"},{"key":"e_1_2_1_42_1","volume-title":"Proceedings of the IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops\u201909)","author":"Choi Wongun","year":"2009","unstructured":"Wongun Choi , Khuram Shahid , and Silvio Savarese . 2009 . What are they doing?: Collective activity classification using spatio-temporal relationship among people . In Proceedings of the IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops\u201909) . IEEE, 1282\u20131289. Wongun Choi, Khuram Shahid, and Silvio Savarese. 2009. What are they doing?: Collective activity classification using spatio-temporal relationship among people. In Proceedings of the IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops\u201909). IEEE, 1282\u20131289."},{"key":"e_1_2_1_43_1","unstructured":"Fran\u00e7ois Chollet et\u00a0al. 2015. Keras. Retrieved from https:\/\/keras.io.  Fran\u00e7ois Chollet et\u00a0al. 2015. Keras. Retrieved from https:\/\/keras.io."},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/MPRV.2008.39"},{"key":"e_1_2_1_45_1","volume-title":"Workshop on Deep Learning (NIPS\u201914)","author":"Chung Junyoung","year":"2014","unstructured":"Junyoung Chung , Caglar Gulcehre , KyungHyun Cho , and Yoshua Bengio . 2014 . Empirical evaluation of gated recurrent neural networks on sequence modeling . In Workshop on Deep Learning (NIPS\u201914) . Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. In Workshop on Deep Learning (NIPS\u201914)."},{"key":"e_1_2_1_46_1","volume-title":"Proceedings of the Conference on Neural Information Processing Systems BigLearn Workshop (NIPS BigLearn Workshop\u201911)","author":"Collobert Ronan","year":"2011","unstructured":"Ronan Collobert , Koray Kavukcuoglu , and Cl\u00e9ment Farabet . 2011 . Torch7: A matlab-like environment for machine learning . In Proceedings of the Conference on Neural Information Processing Systems BigLearn Workshop (NIPS BigLearn Workshop\u201911) . Ronan Collobert, Koray Kavukcuoglu, and Cl\u00e9ment Farabet. 2011. Torch7: A matlab-like environment for machine learning. In Proceedings of the Conference on Neural Information Processing Systems BigLearn Workshop (NIPS BigLearn Workshop\u201911)."},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2765202"},{"key":"e_1_2_1_48_1","volume-title":"Crowley","author":"Cumin Julien","year":"2017","unstructured":"Julien Cumin , Gr\u00e9goire Lefebvre , Fano Ramparany , and James L . Crowley . 2017 . A dataset of routine daily activities in an instrumented home. In Proceedings of the International Conference on Ubiquitous Computing and Ambient Intelligence. Springer , 413\u2013425. Julien Cumin, Gr\u00e9goire Lefebvre, Fano Ramparany, and James L. Crowley. 2017. A dataset of routine daily activities in an instrumented home. In Proceedings of the International Conference on Ubiquitous Computing and Ambient Intelligence. Springer, 413\u2013425."},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357223.3362707"},{"key":"e_1_2_1_50_1","volume-title":"Proceedings of the European Conference on Computer Vision (ECCV\u201918)","author":"Damen Dima","year":"2018","unstructured":"Dima Damen , Hazel Doughty , Giovanni Maria Farinella , Sanja Fidler , Antonino Furnari , Evangelos Kazakos , Davide Moltisanti , Jonathan Munro , Toby Perrett , Will Price , and Michael Wray . 2018 . Scaling egocentric vision: The EPIC-KITCHENS dataset . In Proceedings of the European Conference on Computer Vision (ECCV\u201918) . Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Sanja Fidler, Antonino Furnari, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, and Michael Wray. 2018. Scaling egocentric vision: The EPIC-KITCHENS dataset. In Proceedings of the European Conference on Computer Vision (ECCV\u201918)."},{"key":"e_1_2_1_51_1","unstructured":"Fernando De la Torre Jessica Hodgins Javier Montano Sergio Valcarcel R. Forcada and J. Macey. 2008. Guide to the Carnegie Mellon University Multimodal Activity (cmu-mmac) Database. Technical Report. Carnegie Mellon University Pittsburgh PA.  Fernando De la Torre Jessica Hodgins Javier Montano Sergio Valcarcel R. Forcada and J. Macey. 2008. Guide to the Carnegie Mellon University Multimodal Activity (cmu-mmac) Database. Technical Report. Carnegie Mellon University Pittsburgh PA."},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1561\/2000000039"},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1117\/12.2518469"},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPIN.2016.7743581"},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1515\/sagmb-2015-0098"},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.2019.1800405"},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/ChiCC.2014.6895735"},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2005.10.010"},{"key":"e_1_2_1_59_1","volume-title":"Proceedings of the Asian Conference on Computer Vision. Springer, 331\u2013346","author":"Gammulle Harshala","year":"2018","unstructured":"Harshala Gammulle , Simon Denman , Sridha Sridharan , and Clinton Fookes . 2018 . Multi-level sequence GAN for group activity recognition . In Proceedings of the Asian Conference on Computer Vision. Springer, 331\u2013346 . Harshala Gammulle, Simon Denman, Sridha Sridharan, and Clinton Fookes. 2018. Multi-level sequence GAN for group activity recognition. In Proceedings of the Asian Conference on Computer Vision. Springer, 331\u2013346."},{"key":"e_1_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.5555\/2671164"},{"key":"e_1_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.protcy.2013.04.031"},{"key":"e_1_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.169"},{"key":"e_1_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1161\/01.CIR.101.23.e215"},{"key":"e_1_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.5555\/3086952"},{"key":"e_1_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969125"},{"key":"e_1_2_1_66_1","unstructured":"Alex Graves. 2013. Generating sequences with recurrent neural networks. arXiv:1308.0850. Retrieved from https:\/\/arxiv.org\/abs\/1308.0850.  Alex Graves. 2013. Generating sequences with recurrent neural networks. arXiv:1308.0850. Retrieved from https:\/\/arxiv.org\/abs\/1308.0850."},{"key":"e_1_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1145\/3322241"},{"key":"e_1_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.3390\/s151229821"},{"key":"e_1_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.1109\/PIMRC.2017.8292444"},{"key":"e_1_2_1_70_1","first-page":"2085","article-title":"Locomotion activity recognition using stacked denoising autoencoders","volume":"5","author":"Gu Fuqiang","year":"2018","unstructured":"Fuqiang Gu , Kourosh Khoshelham , Shahrokh Valaee , Jianga Shang , and Rui Zhang . 2018 . Locomotion activity recognition using stacked denoising autoencoders . IEEE IoT J. 5 , 3 (2018), 2085 \u2013 2093 . Fuqiang Gu, Kourosh Khoshelham, Shahrokh Valaee, Jianga Shang, and Rui Zhang. 2018. Locomotion activity recognition using stacked denoising autoencoders. IEEE IoT J. 5, 3 (2018), 2085\u20132093.","journal-title":"IEEE IoT J."},{"key":"e_1_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2018.2871808"},{"key":"e_1_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2017.10.013"},{"key":"e_1_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1145\/3090076"},{"key":"e_1_2_1_74_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2014.04.018"},{"key":"e_1_2_1_75_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2016.7727224"},{"key":"e_1_2_1_76_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning (ICML\u201919)","author":"Haber Eldad","year":"2019","unstructured":"Eldad Haber , Keegan Lensink , Eran Triester , and Lars Ruthotto . 2019 . IMEXnet: Aforward stable deep neural network . In Proceedings of the 36th International Conference on Machine Learning (ICML\u201919) . PMLR 97:2525\u20132534. Eldad Haber, Keegan Lensink, Eran Triester, and Lars Ruthotto. 2019. IMEXnet: Aforward stable deep neural network. In Proceedings of the 36th International Conference on Machine Learning (ICML\u201919). PMLR 97:2525\u20132534."},{"key":"e_1_2_1_77_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6420\/aa9a90"},{"key":"e_1_2_1_78_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2015.2477242"},{"key":"e_1_2_1_79_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2017.11.029"},{"key":"e_1_2_1_80_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_1_81_1","volume-title":"Proceedings of the 13th European Signal Processing Conference. IEEE, 1\u20134.","author":"Helen Marko","year":"2005","unstructured":"Marko Helen and Tuomas Virtanen . 2005 . Separation of drums from polyphonic music using non-negative matrix factorization and support vector machine . In Proceedings of the 13th European Signal Processing Conference. IEEE, 1\u20134. Marko Helen and Tuomas Virtanen. 2005. Separation of drums from polyphonic music using non-negative matrix factorization and support vector machine. In Proceedings of the 13th European Signal Processing Conference. IEEE, 1\u20134."},{"key":"e_1_2_1_82_1","volume-title":"Proceedings of the 32nd AAAI Conference on Artificial Intelligence.","author":"Henderson Peter","year":"2018","unstructured":"Peter Henderson , Riashat Islam , Philip Bachman , Joelle Pineau , Doina Precup , and David Meger . 2018 . Deep reinforcement learning that matters . In Proceedings of the 32nd AAAI Conference on Artificial Intelligence. Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger. 2018. Deep reinforcement learning that matters. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence."},{"key":"e_1_2_1_83_1","volume-title":"Proceedings of the International Conference on Learning Representations","volume":"3","author":"Higgins Irina","year":"2017","unstructured":"Irina Higgins , Loic Matthey , Arka Pal , Christopher Burgess , Xavier Glorot , Matthew Botvinick , Shakir Mohamed , and Alexander Lerchner . 2017 . beta-vae: Learning basic visual concepts with a constrained variational framework . In Proceedings of the International Conference on Learning Representations , Vol. 3 . Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner. 2017. beta-vae: Learning basic visual concepts with a constrained variational framework. In Proceedings of the International Conference on Learning Representations, Vol. 3."},{"key":"e_1_2_1_84_1","volume-title":"et\u00a0al","author":"Hinton Geoffrey","year":"2012","unstructured":"Geoffrey Hinton , Li Deng , Dong Yu , George Dahl , Abdel-rahman Mohamed, Navdeep Jaitly , Andrew Senior , Vincent Vanhoucke , Patrick Nguyen , Brian Kingsbury , et\u00a0al . 2012 . Deep neural networks for acoustic modeling in speech recognition. IEEE Sign. Process. Mag . 29 (2012). Geoffrey Hinton, Li Deng, Dong Yu, George Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Brian Kingsbury, et\u00a0al. 2012. Deep neural networks for acoustic modeling in speech recognition. IEEE Sign. Process. Mag. 29 (2012)."},{"key":"e_1_2_1_85_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2006.18.7.1527"},{"key":"e_1_2_1_86_1","volume-title":"Salakhutdinov","author":"Hinton Geoffrey E.","year":"2006","unstructured":"Geoffrey E. Hinton and Ruslan R . Salakhutdinov . 2006 . Reducing the dimensionality of data with neural networks. Science 313, 5786 (2006), 504\u2013507. Geoffrey E. Hinton and Ruslan R. Salakhutdinov. 2006. Reducing the dimensionality of data with neural networks. Science 313, 5786 (2006), 504\u2013507."},{"key":"e_1_2_1_87_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_2_1_88_1","doi-asserted-by":"publisher","DOI":"10.5121\/ijdkp.2015.5201"},{"key":"e_1_2_1_89_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2016.00405"},{"key":"e_1_2_1_90_1","volume-title":"Distributed Autonomous Robotic Systems","author":"Hughes Dana","unstructured":"Dana Hughes and Nikolaus Correll . 2018. Distributed convolutional neural networks for human activity recognition in wearable robotics . In Distributed Autonomous Robotic Systems . Springer , 619\u2013631. Dana Hughes and Nikolaus Correll. 2018. Distributed convolutional neural networks for human activity recognition in wearable robotics. In Distributed Autonomous Robotic Systems. Springer, 619\u2013631."},{"key":"e_1_2_1_91_1","doi-asserted-by":"publisher","DOI":"10.1145\/1409635.1409638"},{"key":"e_1_2_1_92_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.217"},{"key":"e_1_2_1_93_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12668-013-0088-3"},{"key":"e_1_2_1_94_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10015-017-0422-x"},{"key":"e_1_2_1_95_1","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2654889"},{"key":"e_1_2_1_96_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco_a_01174"},{"key":"e_1_2_1_97_1","volume-title":"Sixth International Conference on Learning Representations (ICLR\u201918)","author":"Karras Tero","year":"2018","unstructured":"Tero Karras , Timo Aila , Samuli Laine , and Jaakko Lehtinen . 2018 . Progressive growing of gans for improved quality . In Sixth International Conference on Learning Representations (ICLR\u201918) . Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. 2018. Progressive growing of gans for improved quality. In Sixth International Conference on Learning Representations (ICLR\u201918)."},{"key":"e_1_2_1_98_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00453"},{"key":"e_1_2_1_99_1","doi-asserted-by":"publisher","DOI":"10.1145\/1959826.1959853"},{"key":"e_1_2_1_100_1","volume-title":"et\u00a0al","author":"Kay Will","year":"2017","unstructured":"Will Kay , Joao Carreira , Karen Simonyan , Brian Zhang , Chloe Hillier , Sudheendra Vijayanarasimhan , Fabio Viola , Tim Green , Trevor Back , Paul Natsev , et\u00a0al . 2017 . The kinetics human action video dataset. arXiv:1705.06950. Retrieved from https:\/\/arxiv.org\/abs\/1705.06950. Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, et\u00a0al. 2017. The kinetics human action video dataset. arXiv:1705.06950. Retrieved from https:\/\/arxiv.org\/abs\/1705.06950."},{"key":"e_1_2_1_101_1","doi-asserted-by":"publisher","DOI":"10.1080\/00031305.2016.1277159"},{"key":"e_1_2_1_102_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2009.04.009"},{"key":"e_1_2_1_103_1","volume-title":"Proceedings of the 2nd International Conference on Learning Representations (ICLR\u201913)","author":"Diederik","unstructured":"Diederik P. Kingma and Max Welling. 2013. Auto-encoding variational bayes . In Proceedings of the 2nd International Conference on Learning Representations (ICLR\u201913) . Diederik P. Kingma and Max Welling. 2013. Auto-encoding variational bayes. In Proceedings of the 2nd International Conference on Learning Representations (ICLR\u201913)."},{"key":"e_1_2_1_104_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995406"},{"key":"e_1_2_1_105_1","volume-title":"Learning Multiple Layers of Features from Tiny Images. Master\u2019s thesis. Department of Computer Science","author":"Krizhevsky Alex","unstructured":"Alex Krizhevsky and Geoffrey Hinton . 2009. Learning Multiple Layers of Features from Tiny Images. Master\u2019s thesis. Department of Computer Science , University of Toronto . Alex Krizhevsky and Geoffrey Hinton. 2009. Learning Multiple Layers of Features from Tiny Images. Master\u2019s thesis. Department of Computer Science, University of Toronto."},{"key":"e_1_2_1_106_1","doi-asserted-by":"publisher","DOI":"10.5555\/2999134.2999257"},{"key":"e_1_2_1_107_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126543"},{"key":"e_1_2_1_108_1","doi-asserted-by":"publisher","DOI":"10.1145\/1964897.1964918"},{"key":"e_1_2_1_109_1","doi-asserted-by":"publisher","DOI":"10.1145\/2750858.2804262"},{"key":"e_1_2_1_110_1","doi-asserted-by":"publisher","DOI":"10.1109\/SURV.2012.110112.00192"},{"key":"e_1_2_1_111_1","doi-asserted-by":"publisher","DOI":"10.5555\/2997189.2997332"},{"key":"e_1_2_1_112_1","volume-title":"Deep learning. Nature 521, 7553","author":"LeCun Yann","year":"2015","unstructured":"Yann LeCun , Yoshua Bengio , and Geoffrey Hinton . 2015. Deep learning. Nature 521, 7553 ( 2015 ), 436. Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. Nature 521, 7553 (2015), 436."},{"key":"e_1_2_1_113_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_2_1_114_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2703082"},{"key":"e_1_2_1_115_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553453"},{"key":"e_1_2_1_116_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-25929-9_70"},{"key":"e_1_2_1_117_1","unstructured":"Fei-Fei Li Justin Johnson and Serena Yeung. 2019. CS231n Convolutional Neural Networks for Visual Recognition. Retrieved from http:\/\/cs231n.github.io\/neural-networks-2\/.  Fei-Fei Li Justin Johnson and Serena Yeung. 2019. CS231n Convolutional Neural Networks for Visual Recognition. Retrieved from http:\/\/cs231n.github.io\/neural-networks-2\/."},{"key":"e_1_2_1_118_1","doi-asserted-by":"publisher","DOI":"10.1145\/2994551.2994569"},{"key":"e_1_2_1_119_1","doi-asserted-by":"publisher","DOI":"10.1145\/3123266.3123365"},{"key":"e_1_2_1_120_1","volume-title":"Proceedings of the European Conference on Computer Vision (ECCV\u201918)","author":"Li Yin","unstructured":"Yin Li , Miao Liu , and James M. Rehg . 2018. In the eye of beholder: Joint learning of gaze and actions in first person video . In Proceedings of the European Conference on Computer Vision (ECCV\u201918) . 619\u2013635. Yin Li, Miao Liu, and James M. Rehg. 2018. In the eye of beholder: Joint learning of gaze and actions in first person video. In Proceedings of the European Conference on Computer Vision (ECCV\u201918). 619\u2013635."},{"key":"e_1_2_1_121_1","volume-title":"Mining Intelligence and Knowledge Exploration","author":"Li Yongmou","unstructured":"Yongmou Li , Dianxi Shi , Bo Ding , and Dongbo Liu . 2014. Unsupervised feature learning for human activity recognition using smartphone sensors . In Mining Intelligence and Knowledge Exploration . Springer , 99\u2013107. Yongmou Li, Dianxi Shi, Bo Ding, and Dongbo Liu. 2014. Unsupervised feature learning for human activity recognition using smartphone sensors. In Mining Intelligence and Knowledge Exploration. Springer, 99\u2013107."},{"key":"e_1_2_1_122_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13634-015-0222-1"},{"key":"e_1_2_1_123_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298958"},{"key":"e_1_2_1_124_1","doi-asserted-by":"publisher","DOI":"10.5555\/1642293.1642417"},{"key":"e_1_2_1_125_1","volume-title":"Ultragesture: Fine-grained gesture sensing and recognition","author":"Ling Kang","year":"2020","unstructured":"Kang Ling , Haipeng Dai , Yuntang Liu , Alex X. Liu , Wei Wang , and Qing Gu . 2020 . Ultragesture: Fine-grained gesture sensing and recognition . IEEE Trans. Mobile Comput . (2020). Kang Ling, Haipeng Dai, Yuntang Liu, Alex X. Liu, Wei Wang, and Qing Gu. 2020. Ultragesture: Fine-grained gesture sensing and recognition. IEEE Trans. Mobile Comput. (2020)."},{"key":"e_1_2_1_126_1","doi-asserted-by":"publisher","DOI":"10.1145\/3123266.3123431"},{"key":"e_1_2_1_127_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2011.307"},{"key":"e_1_2_1_128_1","doi-asserted-by":"publisher","DOI":"10.5555\/2021975.2021992"},{"key":"e_1_2_1_129_1","doi-asserted-by":"publisher","DOI":"10.1145\/2370216.2370270"},{"key":"e_1_2_1_130_1","doi-asserted-by":"publisher","DOI":"10.1145\/1869983.1869992"},{"key":"e_1_2_1_131_1","doi-asserted-by":"publisher","DOI":"10.5555\/3367471.3367473"},{"key":"e_1_2_1_132_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.304"},{"key":"e_1_2_1_133_1","unstructured":"Mathworks. 2016. Deep Learning Toolbox. Retrieved from https:\/\/www.mathworks.com\/products\/deep-learning.html.  Mathworks. 2016. Deep Learning Toolbox. Retrieved from https:\/\/www.mathworks.com\/products\/deep-learning.html."},{"key":"e_1_2_1_134_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnbot.2019.00038"},{"key":"e_1_2_1_135_1","doi-asserted-by":"publisher","DOI":"10.3390\/app7101101"},{"key":"e_1_2_1_136_1","unstructured":"Tomas Mikolov Armand Joulin Sumit Chopra Michael Mathieu and Marc\u2019Aurelio Ranzato. 2014. Learning longer memory in recurrent neural networks. arXiv:1412.7753. Retrieved from https:\/\/arxiv.org\/abs\/1412.7753.  Tomas Mikolov Armand Joulin Sumit Chopra Michael Mathieu and Marc\u2019Aurelio Ranzato. 2014. Learning longer memory in recurrent neural networks. arXiv:1412.7753. Retrieved from https:\/\/arxiv.org\/abs\/1412.7753."},{"key":"e_1_2_1_137_1","unstructured":"Mehdi Mirza and Simon Osindero. 2014. Conditional generative adversarial nets. arXiv:1411.1784. Retrieved from https:\/\/arxiv.org\/abs\/1411.1784.  Mehdi Mirza and Simon Osindero. 2014. Conditional generative adversarial nets. arXiv:1411.1784. Retrieved from https:\/\/arxiv.org\/abs\/1411.1784."},{"key":"e_1_2_1_138_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2006.08.002"},{"key":"e_1_2_1_139_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2014.94"},{"key":"e_1_2_1_140_1","volume-title":"Proceedings of the NIPS Workshop on Deep Learning for Speech Recognition and Related Applications","volume":"1","author":"Dahl George","year":"2009","unstructured":"Abdel-rahman Mohamed, George Dahl , and Geoffrey Hinton . 2009 . Deep belief networks for phone recognition . In Proceedings of the NIPS Workshop on Deep Learning for Speech Recognition and Related Applications , Vol. 1 . Vancouver, Canada, 39. Abdel-rahman Mohamed, George Dahl, and Geoffrey Hinton. 2009. Deep belief networks for phone recognition. In Proceedings of the NIPS Workshop on Deep Learning for Speech Recognition and Related Applications, Vol. 1. Vancouver, Canada, 39."},{"key":"e_1_2_1_141_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2014.2370945"},{"key":"e_1_2_1_142_1","volume-title":"et\u00a0al","author":"M\u00fcller Andreas C.","year":"2016","unstructured":"Andreas C. M\u00fcller , Sarah Guido , et\u00a0al . 2016 . Introduction to Machine Learning with Python : A Guide for Data Scientists. O\u2019Reilly Media , Inc. Andreas C. M\u00fcller, Sarah Guido, et\u00a0al. 2016. Introduction to Machine Learning with Python: A Guide for Data Scientists. O\u2019Reilly Media, Inc."},{"key":"e_1_2_1_143_1","doi-asserted-by":"publisher","DOI":"10.3390\/s17112556"},{"key":"e_1_2_1_144_1","unstructured":"Steve Mutuvi. 2019. Introduction to Machine Learning Model Evaluation. Retrieved from https:\/\/heartbeat.fritz.ai\/introduction-to-machine-learning-model-evaluation-fa859e1b2d7f.  Steve Mutuvi. 2019. Introduction to Machine Learning Model Evaluation. Retrieved from https:\/\/heartbeat.fritz.ai\/introduction-to-machine-learning-model-evaluation-fa859e1b2d7f."},{"key":"e_1_2_1_145_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00024"},{"key":"e_1_2_1_146_1","doi-asserted-by":"publisher","DOI":"10.5555\/2968826.2968874"},{"key":"e_1_2_1_147_1","unstructured":"Andrew Ng. 2019. UFLDL Tutorial: PCA Whitening. Retrieved from http:\/\/ufldl.stanford.edu\/tutorial\/unsupervised\/PCAWhitening\/.  Andrew Ng. 2019. UFLDL Tutorial: PCA Whitening. Retrieved from http:\/\/ufldl.stanford.edu\/tutorial\/unsupervised\/PCAWhitening\/."},{"key":"e_1_2_1_148_1","first-page":"1","article-title":"Sparse autoencoder. CS294A Lect","volume":"72","author":"\u00a0al Andrew Ng","year":"2011","unstructured":"Andrew Ng et \u00a0al . 2011 . Sparse autoencoder. CS294A Lect . Not. 72 , 2011 (2011), 1 \u2013 19 . Andrew Ng et\u00a0al. 2011. Sparse autoencoder. CS294A Lect. Not. 72, 2011 (2011), 1\u201319.","journal-title":"Not."},{"key":"e_1_2_1_149_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2014.10.012"},{"key":"e_1_2_1_150_1","doi-asserted-by":"publisher","DOI":"10.1145\/3267305.3267561"},{"key":"e_1_2_1_151_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2018.03.056"},{"key":"e_1_2_1_152_1","doi-asserted-by":"publisher","DOI":"10.5555\/3305890.3305954"},{"key":"e_1_2_1_153_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2013.6474999"},{"key":"e_1_2_1_154_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995586"},{"key":"e_1_2_1_155_1","volume-title":"TRECVID 2012-an overview of the goals, tasks, data, evaluation mechanisms and metrics. TRECVID Publications. Retrieved","author":"Over Paul","year":"2013","unstructured":"Paul Over , George Awad , Martial Michel , Jonathan Fiscus , Greg Sanders , Barbara Shaw , Wessel Kraaij , Alan F. Smeaton , and Georges Qu\u00e9ot . 2013 . TRECVID 2012-an overview of the goals, tasks, data, evaluation mechanisms and metrics. TRECVID Publications. Retrieved August 22, 2021 http:\/\/www-nlpir.nist.gov\/projects\/tvpubs\/tv.pubs.org.html. Paul Over, George Awad, Martial Michel, Jonathan Fiscus, Greg Sanders, Barbara Shaw, Wessel Kraaij, Alan F. Smeaton, and Georges Qu\u00e9ot. 2013. TRECVID 2012-an overview of the goals, tasks, data, evaluation mechanisms and metrics. TRECVID Publications. Retrieved August 22, 2021 http:\/\/www-nlpir.nist.gov\/projects\/tvpubs\/tv.pubs.org.html."},{"key":"e_1_2_1_156_1","doi-asserted-by":"publisher","DOI":"10.3233\/AIS-160372"},{"key":"e_1_2_1_157_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2905015"},{"key":"e_1_2_1_158_1","volume-title":"31st Conference on Neural Information Processing Systems (NIPS","author":"Paszke Adam","year":"2017","unstructured":"Adam Paszke , Sam Gross , Soumith Chintala , Gregory Chanan , Edward Yang , Zachary DeVito , Zeming Lin , Alban Desmaison , Luca Antiga , and Adam Lerer . 2017 . Automatic differentiation in pytorch . In 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA. Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. 2017. Automatic differentiation in pytorch. In 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA."},{"key":"e_1_2_1_159_1","doi-asserted-by":"publisher","DOI":"10.3390\/s120506155"},{"key":"e_1_2_1_160_1","volume-title":"MARS: Mixed Virtual and Real Wearable Sensors for Human Activity Recognition with Multi-Domain Deep Learning Model","author":"Pei Ling","year":"2021","unstructured":"Ling Pei , Songpengcheng Xia , Lei Chu , Fanyi Xiao , Qi Wu , Wenxian Yu , and Robert Qiu . 2021 . MARS: Mixed Virtual and Real Wearable Sensors for Human Activity Recognition with Multi-Domain Deep Learning Model . IEEE IoT J. ( 2021). Ling Pei, Songpengcheng Xia, Lei Chu, Fanyi Xiao, Qi Wu, Wenxian Yu, and Robert Qiu. 2021. MARS: Mixed Virtual and Real Wearable Sensors for Human Activity Recognition with Multi-Domain Deep Learning Model. IEEE IoT J. (2021)."},{"key":"e_1_2_1_161_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-05408-2_4"},{"key":"e_1_2_1_162_1","doi-asserted-by":"publisher","DOI":"10.5555\/2283516.2283683"},{"key":"e_1_2_1_163_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.325"},{"key":"e_1_2_1_164_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2009.11.014"},{"key":"e_1_2_1_165_1","volume-title":"Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation.","author":"Powers David Martin","year":"2011","unstructured":"David Martin Powers . 2011 . Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation. David Martin Powers. 2011. Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation."},{"key":"e_1_2_1_166_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01249-6_7"},{"key":"e_1_2_1_167_1","doi-asserted-by":"publisher","DOI":"10.1109\/RTSS.2012.62"},{"key":"e_1_2_1_168_1","doi-asserted-by":"publisher","DOI":"10.1145\/1864349.1864393"},{"key":"e_1_2_1_169_1","unstructured":"Alec Radford Luke Metz and Soumith Chintala. 2015. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv:1511.06434. Retrieved from https:\/\/arxiv.org\/abs\/1511.06434.  Alec Radford Luke Metz and Soumith Chintala. 2015. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv:1511.06434. Retrieved from https:\/\/arxiv.org\/abs\/1511.06434."},{"key":"e_1_2_1_170_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351242"},{"key":"e_1_2_1_171_1","doi-asserted-by":"publisher","DOI":"10.1145\/2668332.2668347"},{"key":"e_1_2_1_172_1","doi-asserted-by":"publisher","DOI":"10.1145\/2968219.2971461"},{"key":"e_1_2_1_173_1","doi-asserted-by":"publisher","DOI":"10.1145\/3161174"},{"key":"e_1_2_1_174_1","volume-title":"et\u00a0al","author":"Rajpurkar Pranav","year":"2017","unstructured":"Pranav Rajpurkar , Jeremy Irvin , Kaylie Zhu , Brandon Yang , Hershel Mehta , Tony Duan , Daisy Ding , Aarti Bagul , Curtis Langlotz , Katie Shpanskaya , et\u00a0al . 2017 . Chexnet : Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv:1711.05225. Retrieved from https:\/\/arxiv.org\/abs\/1711.05225. Pranav Rajpurkar, Jeremy Irvin, Kaylie Zhu, Brandon Yang, Hershel Mehta, Tony Duan, Daisy Ding, Aarti Bagul, Curtis Langlotz, Katie Shpanskaya, et\u00a0al. 2017. Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv:1711.05225. Retrieved from https:\/\/arxiv.org\/abs\/1711.05225."},{"key":"e_1_2_1_175_1","doi-asserted-by":"publisher","DOI":"10.5555\/1620092.1620107"},{"key":"e_1_2_1_176_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00138-012-0450-4"},{"key":"e_1_2_1_177_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISWC.2012.13"},{"key":"e_1_2_1_178_1","doi-asserted-by":"publisher","DOI":"10.5555\/2969239.2969250"},{"key":"e_1_2_1_179_1","volume-title":"Andreas Fidjeland, Tim Green, Adri\u00e0 Puigdom\u00e8nech, S\u00e9bastien Racani\u00e8re, Jack Rae, and Fabio Viola.","author":"Reynolds Malcolm","year":"2017","unstructured":"Malcolm Reynolds , Gabriel Barth-Maron , Frederic Besse , Diego de Las Casas , Andreas Fidjeland, Tim Green, Adri\u00e0 Puigdom\u00e8nech, S\u00e9bastien Racani\u00e8re, Jack Rae, and Fabio Viola. 2017 . Open Sourcing Sonnet\u2014A New Library for Constructing Neural Networks . Retrieved from https:\/\/deepmind.com\/blog\/open-sourcing-sonnet\/. Malcolm Reynolds, Gabriel Barth-Maron, Frederic Besse, Diego de Las Casas, Andreas Fidjeland, Tim Green, Adri\u00e0 Puigdom\u00e8nech, S\u00e9bastien Racani\u00e8re, Jack Rae, and Fabio Viola. 2017. Open Sourcing Sonnet\u2014A New Library for Constructing Neural Networks. Retrieved from https:\/\/deepmind.com\/blog\/open-sourcing-sonnet\/."},{"key":"e_1_2_1_180_1","doi-asserted-by":"publisher","DOI":"10.1109\/INSS.2010.5573462"},{"key":"e_1_2_1_181_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-26561-2_6"},{"key":"e_1_2_1_182_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.04.032"},{"key":"e_1_2_1_183_1","doi-asserted-by":"publisher","DOI":"10.1038\/323533a0"},{"key":"e_1_2_1_184_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-statistics-010814-020120"},{"key":"e_1_2_1_185_1","unstructured":"Ruslan Salakhutdinov and Geoffrey Hinton. 2009. Deep boltzmann machines. In Artificial Intelligence and Statistics. 448\u2013455.  Ruslan Salakhutdinov and Geoffrey Hinton. 2009. Deep boltzmann machines. In Artificial Intelligence and Statistics. 448\u2013455."},{"key":"e_1_2_1_186_1","unstructured":"Hojjat Salehinejad Sharan Sankar Joseph Barfett Errol Colak and Shahrokh Valaee. 2017. Recent advances in recurrent neural networks. arXiv:1801.01078. Retrieved from https:\/\/arxiv.org\/abs\/1801.01078.  Hojjat Salehinejad Sharan Sankar Joseph Barfett Errol Colak and Shahrokh Valaee. 2017. Recent advances in recurrent neural networks. arXiv:1801.01078. Retrieved from https:\/\/arxiv.org\/abs\/1801.01078."},{"key":"e_1_2_1_187_1","doi-asserted-by":"publisher","DOI":"10.1145\/3242969.3242985"},{"key":"e_1_2_1_188_1","doi-asserted-by":"publisher","DOI":"10.1145\/2459236.2459254"},{"key":"e_1_2_1_189_1","doi-asserted-by":"publisher","DOI":"10.5555\/1018429.1020906"},{"key":"e_1_2_1_190_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2945397"},{"key":"e_1_2_1_191_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2008.924856"},{"key":"e_1_2_1_192_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAES.2018.2799758"},{"key":"e_1_2_1_193_1","doi-asserted-by":"publisher","DOI":"10.3390\/s151027251"},{"key":"e_1_2_1_194_1","doi-asserted-by":"publisher","DOI":"10.1145\/2370216.2370440"},{"key":"e_1_2_1_195_1","doi-asserted-by":"publisher","DOI":"10.1109\/MPRV.2014.13"},{"key":"e_1_2_1_196_1","doi-asserted-by":"publisher","DOI":"10.3390\/s150102059"},{"key":"e_1_2_1_197_1","doi-asserted-by":"publisher","DOI":"10.1109\/PerCom.2014.6813955"},{"key":"e_1_2_1_198_1","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/679123"},{"key":"e_1_2_1_199_1","volume-title":"3rd International Conference on Learning Representations (ICLR\u201915)","author":"Simonyan Karen","year":"2015","unstructured":"Karen Simonyan and Andrew Zisserman . 2015 . Very deep convolutional networks for large-scale image recognition . In 3rd International Conference on Learning Representations (ICLR\u201915) . Karen Simonyan and Andrew Zisserman. 2015. Very deep convolutional networks for large-scale image recognition. In 3rd International Conference on Learning Representations (ICLR\u201915)."},{"key":"e_1_2_1_200_1","doi-asserted-by":"publisher","DOI":"10.1007\/11853565_13"},{"key":"e_1_2_1_201_1","doi-asserted-by":"publisher","DOI":"10.5555\/3298023.3298186"},{"key":"e_1_2_1_202_1","volume-title":"November, 2012.","author":"Soomro Khurram","year":"2012","unstructured":"Khurram Soomro , Amir Roshan Zamir , and Mubarak Shah . 2012 . UCF101: A Dataset of 101 Human Action Classes From Videos in The Wild, CRCV-TR-12-01 , November, 2012. Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah. 2012. UCF101: A Dataset of 101 Human Action Classes From Videos in The Wild, CRCV-TR-12-01, November, 2012."},{"key":"e_1_2_1_203_1","doi-asserted-by":"publisher","DOI":"10.1145\/2809695.2809718"},{"key":"e_1_2_1_204_1","doi-asserted-by":"publisher","DOI":"10.1145\/3241539.3241568"},{"key":"e_1_2_1_205_1","unstructured":"Mohammed Sunasra. 2019. Performance Metrics for Classification problems in Machine Learning. Retrieved from https:\/\/medium.com\/thalus-ai\/performance-metrics-for-classification-problems-in-machine-learning-part-i-b085d432082b.  Mohammed Sunasra. 2019. Performance Metrics for Classification problems in Machine Learning. Retrieved from https:\/\/medium.com\/thalus-ai\/performance-metrics-for-classification-problems-in-machine-learning-part-i-b085d432082b."},{"key":"e_1_2_1_206_1","doi-asserted-by":"publisher","DOI":"10.5555\/3104482.3104610"},{"key":"e_1_2_1_207_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2017.2761740"},{"key":"e_1_2_1_208_1","doi-asserted-by":"publisher","DOI":"10.5555\/3298023.3298188"},{"key":"e_1_2_1_209_1","volume-title":"Diego Jose Arguello, and Stephen Intille","author":"Tang Qu","year":"2020","unstructured":"Qu Tang , Dinesh John , Binod Thapa-Chhetry , Diego Jose Arguello, and Stephen Intille . 2020 . Posture and physical activity detection: Impact of number of sensors and feature type.Med. Sci. Sports and Exercise ( 2020). Qu Tang, Dinesh John, Binod Thapa-Chhetry, Diego Jose Arguello, and Stephen Intille. 2020. Posture and physical activity detection: Impact of number of sensors and feature type.Med. Sci. Sports and Exercise (2020)."},{"key":"e_1_2_1_210_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2017.8296915"},{"key":"e_1_2_1_211_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2018.2875441"},{"key":"e_1_2_1_212_1","unstructured":"Eclipse Deeplearning4j Development Team. Deeplearning4j: Open-source Distributed Deep Learning for the jvm. Retreived from http:\/\/deeplearning4j.org.  Eclipse Deeplearning4j Development Team. Deeplearning4j: Open-source Distributed Deep Learning for the jvm. Retreived from http:\/\/deeplearning4j.org."},{"key":"e_1_2_1_213_1","volume-title":"Proceedings of Workshop on Machine Learning Systems (LearningSys) in the 29th Annual Conference on Neural Information Processing Systems (NIPS\u201915)","volume":"5","author":"Tokui Seiya","year":"2015","unstructured":"Seiya Tokui , Kenta Oono , Shohei Hido , and Justin Clayton . 2015 . Chainer: A next-generation open source framework for deep learning . In Proceedings of Workshop on Machine Learning Systems (LearningSys) in the 29th Annual Conference on Neural Information Processing Systems (NIPS\u201915) , Vol. 5 . 1\u20136. Seiya Tokui, Kenta Oono, Shohei Hido, and Justin Clayton. 2015. Chainer: A next-generation open source framework for deep learning. In Proceedings of Workshop on Machine Learning Systems (LearningSys) in the 29th Annual Conference on Neural Information Processing Systems (NIPS\u201915), Vol. 5. 1\u20136."},{"key":"e_1_2_1_214_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning (ICML\u201919)","author":"Tran Toan","year":"2019","unstructured":"Toan Tran , Thanh-Toan Do , Ian Reid , and Gustavo Carneiro . 2019 . Bayesian generative active deep learning . In Proceedings of the 36th International Conference on Machine Learning (ICML\u201919) . Toan Tran, Thanh-Toan Do, Ian Reid, and Gustavo Carneiro. 2019. Bayesian generative active deep learning. In Proceedings of the 36th International Conference on Machine Learning (ICML\u201919)."},{"key":"e_1_2_1_215_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/17.6.520"},{"key":"e_1_2_1_216_1","volume-title":"Third workshop on Bayesian Deep Learning (NeurIPS\u201918)","author":"Tschannen Michael","year":"2018","unstructured":"Michael Tschannen , Olivier Bachem , and Mario Lucic . 2018 . Recent advances in autoencoder-based representation learning . In Third workshop on Bayesian Deep Learning (NeurIPS\u201918) . Michael Tschannen, Olivier Bachem, and Mario Lucic. 2018. Recent advances in autoencoder-based representation learning. In Third workshop on Bayesian Deep Learning (NeurIPS\u201918)."},{"key":"e_1_2_1_217_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2676238"},{"key":"e_1_2_1_218_1","doi-asserted-by":"publisher","DOI":"10.1145\/1409635.1409637"},{"key":"e_1_2_1_219_1","doi-asserted-by":"publisher","DOI":"10.5555\/1756006.1953039"},{"key":"e_1_2_1_220_1","volume-title":"Proceedings of 1st International Conference on Information and Communication Technology for Intelligent Systems","volume":"1","author":"Walse Kishor H.","unstructured":"Kishor H. Walse , Rajiv V. Dharaskar , and Vilas M. Thakare . 2016. Pca based optimal ann classifiers for human activity recognition using mobile sensors data . In Proceedings of 1st International Conference on Information and Communication Technology for Intelligent Systems : Volume 1 . Springer, 429\u2013436. Kishor H. Walse, Rajiv V. Dharaskar, and Vilas M. Thakare. 2016. Pca based optimal ann classifiers for human activity recognition using mobile sensors data. In Proceedings of 1st International Conference on Information and Communication Technology for Intelligent Systems: Volume 1. Springer, 429\u2013436."},{"key":"e_1_2_1_221_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2923743"},{"key":"e_1_2_1_222_1","first-page":"2035","article-title":"On spatial diversity in WiFi-based human activity recognition: A deep learning-based approach","volume":"6","author":"Wang Fangxin","year":"2018","unstructured":"Fangxin Wang , Wei Gong , and Jiangchuan Liu . 2018 . On spatial diversity in WiFi-based human activity recognition: A deep learning-based approach . IEEE IoT J. 6 , 2 (2018), 2035 \u2013 2047 . Fangxin Wang, Wei Gong, and Jiangchuan Liu. 2018. On spatial diversity in WiFi-based human activity recognition: A deep learning-based approach. IEEE IoT J. 6, 2 (2018), 2035\u20132047.","journal-title":"IEEE IoT J."},{"key":"e_1_2_1_223_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2018.2825144"},{"key":"e_1_2_1_224_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2018.02.010"},{"key":"e_1_2_1_225_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2016.2635161"},{"key":"e_1_2_1_226_1","doi-asserted-by":"publisher","DOI":"10.3390\/s16020189"},{"key":"e_1_2_1_227_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dcan.2015.02.006"},{"key":"e_1_2_1_228_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2018.12.060"},{"key":"e_1_2_1_229_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISBB.2011.6107697"},{"key":"e_1_2_1_230_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.08.063"},{"key":"e_1_2_1_231_1","volume-title":"the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI\u201920)","author":"Wang Xiaohan","year":"2020","unstructured":"Xiaohan Wang , Yu Wu , Linchao Zhu , and Yi Yang . 2020 . Symbiotic attention with privileged information for ego-centric action recognition . In the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI\u201920) . Xiaohan Wang, Yu Wu, Linchao Zhu, and Yi Yang. 2020. Symbiotic attention with privileged information for ego-centric action recognition. In the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI\u201920)."},{"key":"e_1_2_1_232_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155402"},{"key":"e_1_2_1_233_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287071"},{"key":"e_1_2_1_234_1","volume-title":"Proceedings of the Workshops at the 29th AAAI Conference on Artificial Intelligence.","author":"Wang Zhiguang","year":"2015","unstructured":"Zhiguang Wang and Tim Oates . 2015 . Encoding time series as images for visual inspection and classification using tiled convolutional neural networks . In Proceedings of the Workshops at the 29th AAAI Conference on Artificial Intelligence. Zhiguang Wang and Tim Oates. 2015. Encoding time series as images for visual inspection and classification using tiled convolutional neural networks. In Proceedings of the Workshops at the 29th AAAI Conference on Artificial Intelligence."},{"key":"e_1_2_1_235_1","doi-asserted-by":"publisher","DOI":"10.1145\/3439723"},{"key":"e_1_2_1_236_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2017.1700143"},{"key":"e_1_2_1_237_1","doi-asserted-by":"publisher","DOI":"10.1109\/TETC.2018.2790080"},{"key":"e_1_2_1_238_1","doi-asserted-by":"publisher","DOI":"10.5555\/3298023.3298056"},{"key":"e_1_2_1_239_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2018.2834155"},{"key":"e_1_2_1_240_1","doi-asserted-by":"publisher","DOI":"10.5555\/1735835.1735838"},{"key":"e_1_2_1_241_1","doi-asserted-by":"publisher","DOI":"10.5555\/2832747.2832806"},{"key":"e_1_2_1_242_1","first-page":"10763","article-title":"Learning gestures from WiFi: A siamese recurrent convolutional architecture","volume":"6","author":"Yang Jianfei","year":"2019","unstructured":"Jianfei Yang , Han Zou , Yuxun Zhou , and Lihua Xie . 2019 . Learning gestures from WiFi: A siamese recurrent convolutional architecture . IEEE IoT J. 6 , 6 (2019), 10763 \u2013 10772 . Jianfei Yang, Han Zou, Yuxun Zhou, and Lihua Xie. 2019. Learning gestures from WiFi: A siamese recurrent convolutional architecture. IEEE IoT J. 6, 6 (2019), 10763\u201310772.","journal-title":"IEEE IoT J."},{"key":"e_1_2_1_243_1","doi-asserted-by":"publisher","DOI":"10.1002\/wcm.2706"},{"key":"e_1_2_1_244_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2017.1700082"},{"key":"e_1_2_1_245_1","doi-asserted-by":"publisher","DOI":"10.5555\/1786014.1786017"},{"key":"e_1_2_1_246_1","doi-asserted-by":"publisher","DOI":"10.4108\/icst.mobicase.2014.257786"},{"key":"e_1_2_1_247_1","volume-title":"the 36th International Conference on Machine Learning (ICML\u201919)","author":"Zhang Han","year":"2019","unstructured":"Han Zhang , Ian Goodfellow , Dimitris Metaxas , and Augustus Odena . 2019 . Self-attention generative adversarial networks . In the 36th International Conference on Machine Learning (ICML\u201919) . Han Zhang, Ian Goodfellow, Dimitris Metaxas, and Augustus Odena. 2019. Self-attention generative adversarial networks. In the 36th International Conference on Machine Learning (ICML\u201919)."},{"key":"e_1_2_1_248_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3026732"},{"key":"e_1_2_1_249_1","doi-asserted-by":"publisher","DOI":"10.1145\/2370216.2370438"},{"key":"e_1_2_1_250_1","article-title":"A survey on deep learning based brain computer interface: Recent advances and new frontiers","volume":"10","author":"Zhang Xiang","year":"2020","unstructured":"Xiang Zhang , Lina Yao , Xianzhi Wang , Jessica Monaghan , and David Mcalpine . 2020 . A survey on deep learning based brain computer interface: Recent advances and new frontiers . J Neural Eng. DOI : 10 .1088\/1741-2552\/abc902. Epub ahead of print. PMID: 33171452. 10.1088\/1741-2552 Xiang Zhang, Lina Yao, Xianzhi Wang, Jessica Monaghan, and David Mcalpine. 2020. A survey on deep learning based brain computer interface: Recent advances and new frontiers. J Neural Eng. DOI:10.1088\/1741-2552\/abc902. Epub ahead of print. PMID: 33171452.","journal-title":"J Neural Eng. DOI"},{"key":"e_1_2_1_251_1","doi-asserted-by":"publisher","DOI":"10.1145\/3144457.3144477"},{"key":"e_1_2_1_252_1","doi-asserted-by":"publisher","DOI":"10.1109\/TASL.2012.2229986"},{"key":"e_1_2_1_253_1","volume-title":"the 5th International Conference on Learning Representations (ICLR\u201917)","author":"Zhao Junbo","year":"2017","unstructured":"Junbo Zhao , Michael Mathieu , and Yann LeCun . 2017 . Energy-based generative adversarial network . In the 5th International Conference on Learning Representations (ICLR\u201917) . Junbo Zhao, Michael Mathieu, and Yann LeCun. 2017. Energy-based generative adversarial network. In the 5th International Conference on Learning Representations (ICLR\u201917)."},{"key":"e_1_2_1_254_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02943243"},{"key":"e_1_2_1_255_1","volume-title":"Identifying Stable Patterns over Time for Emotion Recognition from EEG","author":"Zheng Weilong","year":"2018","unstructured":"Weilong Zheng , Jiayi Zhu , and Baoliang Lu. 2018. Identifying Stable Patterns over Time for Emotion Recognition from EEG . IEEE Trans. Affect. Comput . ( 2018 ). https:\/\/doi.org\/10.1109\/TAFFC.2017.2712143 10.1109\/TAFFC.2017.2712143 Weilong Zheng, Jiayi Zhu, and Baoliang Lu. 2018. Identifying Stable Patterns over Time for Emotion Recognition from EEG. IEEE Trans. Affect. Comput. (2018). https:\/\/doi.org\/10.1109\/TAFFC.2017.2712143"},{"key":"e_1_2_1_256_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICME.2014.6890166"},{"key":"e_1_2_1_257_1","doi-asserted-by":"publisher","DOI":"10.1145\/1658373.1658374"},{"key":"e_1_2_1_258_1","doi-asserted-by":"publisher","DOI":"10.1145\/1409635.1409677"},{"key":"e_1_2_1_259_1","doi-asserted-by":"publisher","DOI":"10.1145\/1409635.1409677"},{"key":"e_1_2_1_260_1","doi-asserted-by":"publisher","DOI":"10.3390\/s19030621"},{"key":"e_1_2_1_261_1","doi-asserted-by":"publisher","DOI":"10.1145\/2426656.2426668"},{"key":"e_1_2_1_262_1","volume-title":"Proceedings of the IEEE International Conference on Computer Vision. 2223\u20132232","author":"Zhu Jun-Yan","unstructured":"Jun-Yan Zhu , Taesung Park , Phillip Isola , and Alexei A. Efros . 2017. Unpaired image-to-image translation using cycle-consistent adversarial networks . In Proceedings of the IEEE International Conference on Computer Vision. 2223\u20132232 . Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A. Efros. 2017. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE International Conference on Computer Vision. 2223\u20132232."},{"key":"e_1_2_1_263_1","doi-asserted-by":"publisher","DOI":"10.5555\/3045118.3045289"},{"key":"e_1_2_1_264_1","volume-title":"Proceedings of the IEEE International Conference on Communications (ICC\u201918)","author":"Zou Han","unstructured":"Han Zou , Yuxun Zhou , Jianfei Yang , Hao Jiang , Lihua Xie , and Costas J. Spanos . 2018. Deepsense: Device-free human activity recognition via autoencoder long-term recurrent convolutional network . In Proceedings of the IEEE International Conference on Communications (ICC\u201918) . IEEE, 1\u20136. Han Zou, Yuxun Zhou, Jianfei Yang, Hao Jiang, Lihua Xie, and Costas J. Spanos. 2018. Deepsense: Device-free human activity recognition via autoencoder long-term recurrent convolutional network. In Proceedings of the IEEE International Conference on Communications (ICC\u201918). IEEE, 1\u20136."}],"container-title":["ACM Computing Surveys"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3472290","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3472290","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:10:01Z","timestamp":1750183801000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3472290"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,4]]},"references-count":264,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2022,11,30]]}},"alternative-id":["10.1145\/3472290"],"URL":"https:\/\/doi.org\/10.1145\/3472290","relation":{},"ISSN":["0360-0300","1557-7341"],"issn-type":[{"value":"0360-0300","type":"print"},{"value":"1557-7341","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,4]]},"assertion":[{"value":"2020-06-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-06-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-10-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}