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ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2021,3,19]]},"abstract":"<jats:p>Machine learning and deep learning have shown great promise in mobile sensing applications, including Human Activity Recognition. However, the performance of such models in real-world settings largely depends on the availability of large datasets that captures diverse behaviors. Recently, studies in computer vision and natural language processing have shown that leveraging massive amounts of unlabeled data enables performance on par with state-of-the-art supervised models.<\/jats:p>\n          <jats:p>In this work, we present SelfHAR, a semi-supervised model that effectively learns to leverage unlabeled mobile sensing datasets to complement small labeled datasets. Our approach combines teacher-student self-training, which distills the knowledge of unlabeled and labeled datasets while allowing for data augmentation, and multi-task self-supervision, which learns robust signal-level representations by predicting distorted versions of the input.<\/jats:p>\n          <jats:p>We evaluated SelfHAR on various HAR datasets and showed state-of-the-art performance over supervised and previous semi-supervised approaches, with up to 12% increase in F1 score using the same number of model parameters at inference. Furthermore, SelfHAR is data-efficient, reaching similar performance using up to 10 times less labeled data compared to supervised approaches. Our work not only achieves state-of-the-art performance in a diverse set of HAR datasets, but also sheds light on how pre-training tasks may affect downstream performance.<\/jats:p>","DOI":"10.1145\/3448112","type":"journal-article","created":{"date-parts":[[2021,3,30]],"date-time":"2021-03-30T18:56:41Z","timestamp":1617130601000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":123,"title":["SelfHAR"],"prefix":"10.1145","volume":"5","author":[{"given":"Chi Ian","family":"Tang","sequence":"first","affiliation":[{"name":"Department of Computer Science and Technology, University of Cambridge, Cambridge, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ignacio","family":"Perez-Pozuelo","sequence":"additional","affiliation":[{"name":"Dept of Medicine, University of Cambridge, Cambridge, Cambridgeshire, UK, The Alan Turing Institute, London, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dimitris","family":"Spathis","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, University of Cambridge, Cambridge, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Soren","family":"Brage","sequence":"additional","affiliation":[{"name":"MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nick","family":"Wareham","sequence":"additional","affiliation":[{"name":"MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cecilia","family":"Mascolo","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, University of Cambridge, Cambridge, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,3,30]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"3","article-title":"A public domain dataset for human activity recognition using smartphones","volume":"3","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 Esann , Vol. 3. 3 . 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 Esann, Vol. 3. 3.","journal-title":"Esann"},{"key":"e_1_2_1_2_1","unstructured":"Philip Bachman R Devon Hjelm and William Buchwalter. 2019. Learning representations by maximizing mutual information across views. In Advances in Neural Information Processing Systems. 15535--15545.  Philip Bachman R Devon Hjelm and William Buchwalter. 2019. Learning representations by maximizing mutual information across views. In Advances in Neural Information Processing Systems. 15535--15545."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmcj.2014.05.006"},{"key":"e_1_2_1_4_1","volume-title":"International Conference on Information and Communication Technologies for Ageing Well and e-Health. 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A simple framework for contrastive learning of visual representations. arXiv preprint arXiv:2002.05709 ( 2020 ). Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. arXiv preprint arXiv:2002.05709 (2020)."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_2_1_7_1","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","volume":"1","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin , Ming-Wei Chang , Kenton Lee , and Kristina Toutanova . 2019 . BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding . In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , Volume 1 (Long and Short Papers). 4171--4186. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 4171--4186."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.226"},{"key":"e_1_2_1_9_1","volume-title":"An empirical investigation of catastrophic forgetting in gradient-based neural networks. arXiv preprint arXiv:1312.6211","author":"Goodfellow Ian J","year":"2013","unstructured":"Ian J Goodfellow , Mehdi Mirza , Da Xiao , Aaron Courville , and Yoshua Bengio . 2013. An empirical investigation of catastrophic forgetting in gradient-based neural networks. arXiv preprint arXiv:1312.6211 ( 2013 ). Ian J Goodfellow, Mehdi Mirza, Da Xiao, Aaron Courville, and Yoshua Bengio. 2013. An empirical investigation of catastrophic forgetting in gradient-based neural networks. arXiv preprint arXiv:1312.6211 (2013)."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/RTCSA.2007.17"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3090076"},{"key":"e_1_2_1_12_1","volume-title":"convolutional, and recurrent models for human activity recognition using wearables. arXiv preprint arXiv:1604.08880","author":"Hammerla Nils Y","year":"2016","unstructured":"Nils Y Hammerla , Shane Halloran , and Thomas Pl\u00f6tz . 2016. Deep , convolutional, and recurrent models for human activity recognition using wearables. arXiv preprint arXiv:1604.08880 ( 2016 ). Nils Y Hammerla, Shane Halloran, and Thomas Pl\u00f6tz. 2016. Deep, convolutional, and recurrent models for human activity recognition using wearables. arXiv preprint arXiv:1604.08880 (2016)."},{"key":"e_1_2_1_13_1","volume-title":"Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531","author":"Hinton Geoffrey","year":"2015","unstructured":"Geoffrey Hinton , Oriol Vinyals , and Jeff Dean . 2015. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 ( 2015 ). Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)."},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073659"},{"key":"e_1_2_1_15_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba . 2014 . Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_2_1_16_1","volume-title":"Danilo Jimenez Rezende, and Max Welling","author":"Kingma Durk P","year":"2014","unstructured":"Durk P Kingma , Shakir Mohamed , Danilo Jimenez Rezende, and Max Welling . 2014 . Semi-supervised learning with deep generative models. In Advances in neural information processing systems. 3581--3589. Durk P Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling. 2014. Semi-supervised learning with deep generative models. In Advances in neural information processing systems. 3581--3589."},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3161201"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/1964897.1964918"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/SURV.2012.110112.00192"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.79"},{"key":"e_1_2_1_21_1","volume-title":"Sang Min Yoon, and Heeryon Cho","author":"Lee Song-Mi","year":"2017","unstructured":"Song-Mi Lee , Sang Min Yoon, and Heeryon Cho . 2017 . Human activity recognition from accelerometer data using Convolutional Neural Network. In 2017 ieee international conference on big data and smart computing (bigcomp). IEEE , 131--134. Song-Mi Lee, Sang Min Yoon, and Heeryon Cho. 2017. Human activity recognition from accelerometer data using Convolutional Neural Network. In 2017 ieee international conference on big data and smart computing (bigcomp). 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A survey on bias and fairness in machine learning. arXiv preprint arXiv:1908.09635 ( 2019 ). Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. 2019. A survey on bias and fairness in machine learning. arXiv preprint arXiv:1908.09635 (2019)."},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.3390\/app7101101"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46448-0_32"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46466-4_5"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2018.03.056"},{"key":"e_1_2_1_30_1","volume-title":"Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748","author":"van den Oord Aaron","year":"2018","unstructured":"Aaron van den Oord , Yazhe Li , and Oriol Vinyals . 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 ( 2018 ). Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)."},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.222"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1017\/S000711451500269X"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3214277"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3161174"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1177\/1550147716665520"},{"key":"e_1_2_1_37_1","volume-title":"Human activity recognition with smartphone sensors using deep learning neural networks. Expert systems with applications 59","author":"Ronao Charissa Ann","year":"2016","unstructured":"Charissa Ann Ronao and Sung-Bae Cho . 2016. Human activity recognition with smartphone sensors using deep learning neural networks. Expert systems with applications 59 ( 2016 ), 235--244. Charissa Ann Ronao and Sung-Bae Cho. 2016. Human activity recognition with smartphone sensors using deep learning neural networks. Expert systems with applications 59 (2016), 235--244."},{"key":"e_1_2_1_38_1","doi-asserted-by":"crossref","unstructured":"Chuck Rosenberg Martial Hebert and Henry Schneiderman. 2005. Semi-supervised self-training of object detection models. (2005).  Chuck Rosenberg Martial Hebert and Henry Schneiderman. 2005. Semi-supervised self-training of object detection models. (2005).","DOI":"10.1109\/ACVMOT.2005.107"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3328932"},{"key":"e_1_2_1_40_1","volume-title":"Sense and Learn: Self-Supervision for Omnipresent Sensors. arXiv preprint arXiv:2009.13233","author":"Saeed Aaqib","year":"2020","unstructured":"Aaqib Saeed , Victor Ungureanu , and Beat Gfeller . 2020. Sense and Learn: Self-Supervision for Omnipresent Sensors. arXiv preprint arXiv:2009.13233 ( 2020 ). Aaqib Saeed, Victor Ungureanu, and Beat Gfeller. 2020. Sense and Learn: Self-Supervision for Omnipresent Sensors. arXiv preprint arXiv:2009.13233 (2020)."},{"key":"e_1_2_1_41_1","volume-title":"Self-supervised ecg representation learning for emotion recognition. arXiv preprint arXiv:2002.03898","author":"Sarkar Pritam","year":"2020","unstructured":"Pritam Sarkar and Ali Etemad . 2020. Self-supervised ecg representation learning for emotion recognition. arXiv preprint arXiv:2002.03898 ( 2020 ). Pritam Sarkar and Ali Etemad. 2020. Self-supervised ecg representation learning for emotion recognition. arXiv preprint arXiv:2002.03898 (2020)."},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/DICTA.2016.7797053"},{"key":"e_1_2_1_43_1","volume-title":"Self-supervised transfer learning of physiological representations from free-living wearable data. arXiv preprint arXiv:2011.12121","author":"Spathis Dimitris","year":"2020","unstructured":"Dimitris Spathis , Ignacio Perez-Pozuelo , Soren Brage , Nicholas J Wareham , and Cecilia Mascolo . 2020. Self-supervised transfer learning of physiological representations from free-living wearable data. arXiv preprint arXiv:2011.12121 ( 2020 ). Dimitris Spathis, Ignacio Perez-Pozuelo, Soren Brage, Nicholas J Wareham, and Cecilia Mascolo. 2020. Self-supervised transfer learning of physiological representations from free-living wearable data. arXiv preprint arXiv:2011.12121 (2020)."},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISWC.2008.4911590"},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/2809695.2809718"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00049"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01424-7_27"},{"key":"e_1_2_1_48_1","volume-title":"Domain adaptation in computer vision applications","author":"Tommasi Tatiana","unstructured":"Tatiana Tommasi , Novi Patricia , Barbara Caputo , and Tinne Tuytelaars . 2017. A deeper look at dataset bias . In Domain adaptation in computer vision applications . Springer , 37--55. Tatiana Tommasi, Novi Patricia, Barbara Caputo, and Tinne Tuytelaars. 2017. A deeper look at dataset bias. In Domain adaptation in computer vision applications. Springer, 37--55."},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995347"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3136755.3136817"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-019-05855-6"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1152\/japplphysiol.00984.2012"},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390294"},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0167472"},{"key":"e_1_2_1_55_1","volume-title":"Billion-scale semi-supervised learning for image classification. arXiv preprint arXiv:1905.00546","author":"Yalniz I Zeki","year":"2019","unstructured":"I Zeki Yalniz , Herv\u00e9 J\u00e9gou , Kan Chen , Manohar Paluri , and Dhruv Mahajan . 2019. Billion-scale semi-supervised learning for image classification. arXiv preprint arXiv:1905.00546 ( 2019 ). I Zeki Yalniz, Herv\u00e9 J\u00e9gou, Kan Chen, Manohar Paluri, and Dhruv Mahajan. 2019. Billion-scale semi-supervised learning for image classification. arXiv preprint arXiv:1905.00546 (2019)."},{"key":"e_1_2_1_56_1","volume-title":"Twenty-Fourth International Joint Conference on Artificial Intelligence.","author":"Yang Jianbo","year":"2015","unstructured":"Jianbo Yang , Minh Nhut Nguyen , Phyo Phyo San , Xiao Li Li , and Shonali Krishnaswamy . 2015 . Deep convolutional neural networks on multichannel time series for human activity recognition . In Twenty-Fourth International Joint Conference on Artificial Intelligence. Jianbo Yang, Minh Nhut Nguyen, Phyo Phyo San, Xiao Li Li, and Shonali Krishnaswamy. 2015. Deep convolutional neural networks on multichannel time series for human activity recognition. In Twenty-Fourth International Joint Conference on Artificial Intelligence."},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-74873-1_47"},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/3264954"},{"key":"e_1_2_1_59_1","volume-title":"33rd annual meeting of the association for computational linguistics. 189--196.","author":"Yarowsky David","unstructured":"David Yarowsky . 1995. Unsupervised word sense disambiguation rivaling supervised methods . In 33rd annual meeting of the association for computational linguistics. 189--196. David Yarowsky. 1995. Unsupervised word sense disambiguation rivaling supervised methods. In 33rd annual meeting of the association for computational linguistics. 189--196."},{"key":"e_1_2_1_60_1","unstructured":"Jason Yosinski Jeff Clune Yoshua Bengio and Hod Lipson. 2014. How transferable are features in deep neural networks?. In Advances in neural information processing systems. 3320--3328.  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