{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T13:25:47Z","timestamp":1769347547498,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":37,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T00:00:00Z","timestamp":1695254400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"German Academic Exchange Service","award":["91831212"],"award-info":[{"award-number":["91831212"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,9,21]]},"DOI":"10.1145\/3615834.3615842","type":"proceedings-article","created":{"date-parts":[[2023,10,11]],"date-time":"2023-10-11T22:45:48Z","timestamp":1697064348000},"page":"1-7","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Exploring the Benefits of Time Series Data Augmentation for Wearable Human Activity Recognition."],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0240-1708","authenticated-orcid":false,"given":"Md Abid","family":"Hasan","sequence":"first","affiliation":[{"name":"Institute of Medical Informatics, University of Luebeck, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2110-4207","authenticated-orcid":false,"given":"Frederic","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Medical Informatics, University of Luebeck, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2137-8363","authenticated-orcid":false,"given":"Artur","family":"Piet","sequence":"additional","affiliation":[{"name":"Institute of Medical Informatics, University of Luebeck, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8610-2291","authenticated-orcid":false,"given":"Philip","family":"Gouverneur","sequence":"additional","affiliation":[{"name":"Institute of Medical Informatics, University of Luebeck, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4581-4107","authenticated-orcid":false,"given":"Muhammad Tausif","family":"Irshad","sequence":"additional","affiliation":[{"name":"Institute of Medical Informatics, University of Luebeck, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4877-8287","authenticated-orcid":false,"given":"Marcin","family":"Grzegorzek","sequence":"additional","affiliation":[{"name":"Institute of Medical Informatics, University of Luebeck, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,10,11]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.3390\/electronics11172737"},{"key":"e_1_3_2_1_2_1","volume-title":"Training with noise is equivalent to Tikhonov regularization. Neural computation 7, 1","author":"Bishop M","year":"1995","unstructured":"Chris\u00a0M Bishop. 1995. Training with noise is equivalent to Tikhonov regularization. Neural computation 7, 1 (1995), 108\u2013116."},{"key":"e_1_3_2_1_3_1","volume-title":"SMOTE for high-dimensional class-imbalanced data. BMC bioinformatics 14","author":"Blagus Rok","year":"2013","unstructured":"Rok Blagus and Lara Lusa. 2013. SMOTE for high-dimensional class-imbalanced data. BMC bioinformatics 14 (2013), 1\u201316."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","unstructured":"David\u00a0R Brillinger. 2001. Time series: data analysis and theory. SIAM.","DOI":"10.1137\/1.9780898719246"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2014.2308321"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1613\/jair.953"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2019.1911747"},{"key":"e_1_3_2_1_8_1","first-page":"52","article-title":"Exploring generative data augmentation in multivariate time series forecasting: opportunities and challenges","volume":"137","author":"Debnath Ankur","year":"2021","unstructured":"Ankur Debnath, Govind Waghmare, Hardik Wadhwa, Siddhartha Asthana, and Ankur Arora. 2021. Exploring generative data augmentation in multivariate time series forecasting: opportunities and challenges. Solar-Energy 137 (2021), 52\u2013560.","journal-title":"Solar-Energy"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/CSCI49370.2019.00046"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2017.106"},{"key":"e_1_3_2_1_11_1","volume-title":"Robusttad: Robust time series anomaly detection via decomposition and convolutional neural networks. arXiv preprint arXiv:2002.09545","author":"Gao Jingkun","year":"2020","unstructured":"Jingkun Gao, Xiaomin Song, Qingsong Wen, Pichao Wang, Liang Sun, and Huan Xu. 2020. Robusttad: Robust time series anomaly detection via decomposition and convolutional neural networks. arXiv preprint arXiv:2002.09545 (2020)."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3422622"},{"key":"e_1_3_2_1_13_1","volume-title":"Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30","author":"Heusel Martin","year":"2017","unstructured":"Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. 2017. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_2_1_14_1","volume-title":"BSDGAN: Balancing Sensor Data Generative Adversarial Networks for Human Activity Recognition. arXiv preprint arXiv:2208.03647","author":"Hu Yifan","year":"2022","unstructured":"Yifan Hu and Yu Wang. 2022. BSDGAN: Balancing Sensor Data Generative Adversarial Networks for Human Activity Recognition. arXiv preprint arXiv:2208.03647 (2022)."},{"key":"e_1_3_2_1_15_1","volume-title":"Different approaches for human activity recognition: A survey. arXiv preprint arXiv:1906.05074","author":"Hussain Zawar","year":"2019","unstructured":"Zawar Hussain, Michael Sheng, and Wei\u00a0Emma Zhang. 2019. Different approaches for human activity recognition: A survey. arXiv preprint arXiv:1906.05074 (2019)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2020.102738"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0254841"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR48806.2021.9412812"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2019.08.100"},{"key":"e_1_3_2_1_20_1","volume-title":"ECML\/PKDD workshop on advanced analytics and learning on temporal data.","author":"Le\u00a0Guennec Arthur","year":"2016","unstructured":"Arthur Le\u00a0Guennec, Simon Malinowski, and Romain Tavenard. 2016. Data augmentation for time series classification using convolutional neural networks. In ECML\/PKDD workshop on advanced analytics and learning on temporal data."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3410530.3414367"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/86.2.301"},{"key":"e_1_3_2_1_23_1","volume-title":"Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784","author":"Mirza Mehdi","year":"2014","unstructured":"Mehdi Mirza and Simon Osindero. 2014. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.3390\/s16010115"},{"key":"e_1_3_2_1_25_1","volume-title":"ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction, Vol.\u00a036","author":"Rashid M","year":"2019","unstructured":"Khandakar\u00a0M Rashid and Joseph Louis. 2019. Window-warping: a time series data augmentation of IMU data for construction equipment activity identification. In ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction, Vol.\u00a036. IAARC Publications, 651\u2013657."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/INSS.2010.5573462"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/TASSP.1978.1163055"},{"key":"e_1_3_2_1_28_1","volume-title":"Conditional GAN for timeseries generation. arXiv preprint arXiv:2006.16477","author":"Smith E","year":"2020","unstructured":"Kaleb\u00a0E Smith and Anthony\u00a0O Smith. 2020. Conditional GAN for timeseries generation. arXiv preprint arXiv:2006.16477 (2020)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.3390\/s18092892"},{"key":"e_1_3_2_1_30_1","first-page":"50","article-title":"Applications and challenges of human activity recognition using sensors in a smart environment","volume":"2","author":"Sunny T","year":"2015","unstructured":"Jubil\u00a0T Sunny, Sonia\u00a0Mary George, Jubilant\u00a0J Kizhakkethottam, Jubil\u00a0T Sunny, Sonia\u00a0Mary George, and Jubilant\u00a0J Kizhakkethottam. 2015. Applications and challenges of human activity recognition using sensors in a smart environment. IJIRST Int. J. Innov. Res. Sci. Technol 2 (2015), 50\u201357.","journal-title":"IJIRST Int. J. Innov. Res. Sci. Technol"},{"key":"e_1_3_2_1_31_1","volume-title":"Deep convolutional neural networks and data augmentation for acoustic event detection. arXiv preprint arXiv:1604.07160","author":"Takahashi Naoya","year":"2016","unstructured":"Naoya Takahashi, Michael Gygli, Beat Pfister, and Luc Van\u00a0Gool. 2016. Deep convolutional neural networks and data augmentation for acoustic event detection. arXiv preprint arXiv:1604.07160 (2016)."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3136755.3136817"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2018.8489106"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.04.057"},{"key":"e_1_3_2_1_35_1","volume-title":"Time series data augmentation for deep learning: A survey. arXiv preprint arXiv:2002.12478","author":"Wen Qingsong","year":"2020","unstructured":"Qingsong Wen, Liang Sun, Fan Yang, Xiaomin Song, Jingkun Gao, Xue Wang, and Huan Xu. 2020. Time series data augmentation for deep learning: A survey. arXiv preprint arXiv:2002.12478 (2020)."},{"key":"e_1_3_2_1_36_1","volume-title":"Time-series generative adversarial networks. Advances in neural information processing systems 32","author":"Yoon Jinsung","year":"2019","unstructured":"Jinsung Yoon, Daniel Jarrett, and Mihaela Van\u00a0der Schaar. 2019. Time-series generative adversarial networks. Advances in neural information processing systems 32 (2019)."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/334"}],"event":{"name":"iWOAR 2023: 8th international Workshop on Sensor-Based Activity Recognition and Artificial Intelligence","location":"L\u00fcbeck Germany","acronym":"iWOAR 2023"},"container-title":["Proceedings of the 8th international Workshop on Sensor-Based Activity Recognition and Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3615834.3615842","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3615834.3615842","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:10:17Z","timestamp":1750295417000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3615834.3615842"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,21]]},"references-count":37,"alternative-id":["10.1145\/3615834.3615842","10.1145\/3615834"],"URL":"https:\/\/doi.org\/10.1145\/3615834.3615842","relation":{},"subject":[],"published":{"date-parts":[[2023,9,21]]},"assertion":[{"value":"2023-10-11","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}