{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T14:30:24Z","timestamp":1775745024252,"version":"3.50.1"},"reference-count":50,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2023,4,18]],"date-time":"2023-04-18T00:00:00Z","timestamp":1681776000000},"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":["62072061 and U20A20176"],"award-info":[{"award-number":["62072061 and U20A20176"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Comput. Healthcare"],"published-print":{"date-parts":[[2023,4,30]]},"abstract":"<jats:p>Deep learning has achieved significant success on intelligent medical treatments, such as automatic diagnosis and analysis of medical data. To train an automatic diagnosis system with high accuracy and strong robustness in healthcare, sufficient training data are required when using deep learning-based methods. However, given that the data collected by sensors that are embedded in medical or mobile devices are inadequate, it is challenging to train an effective and efficient classification model with state-of-the-art performance. Inspired by generative adversarial networks (GANs), we propose TS-GAN, a Time-series GAN architecture based on long short-term memory (LSTM) networks for sensor-based health data augmentation, thereby improving the performance of deep learning-based classification models. TS-GAN aims to learn a generative model that creates time-series data with the same space and time dependence as the real data. Specifically, we design an LSTM-based generator for creating realistic data and an LSTM-based discriminator for determining how similar the generated data are to real data. In particular, we design a sequential-squeeze-and-excitation module in the LSTM-based discriminator to better understand space dependence of real data, and apply the gradient penalty originated from Wasserstein GANs in the training process to stabilize the optimization. We conduct comparative experiments to evaluate the performance of TS-GAN with TimeGAN, C-RNN-GAN and Conditional Wasserstein GANs through discriminator loss, maximum mean discrepancy, visualization methods and classification accuracy on health datasets of ECG_200, NonInvasiveFatalECG_Thorax1, and mHealth, respectively. The experimental results show that TS-GAN exceeds other state-of-the-art time-series GANs in almost all the evaluation metrics, and the classifier trained on synthetic datasets generated by TS-GAN achieves the highest classification accuracy of 97.50% on ECG_200, 94.12% on NonInvasiveFatalECG_Thorax1, and 98.12% on mHealth, respectively.<\/jats:p>","DOI":"10.1145\/3583593","type":"journal-article","created":{"date-parts":[[2023,2,8]],"date-time":"2023-02-08T13:30:44Z","timestamp":1675863044000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":61,"title":["TS-GAN: Time-series GAN for Sensor-based Health Data Augmentation"],"prefix":"10.1145","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3033-9211","authenticated-orcid":false,"given":"Zhenyu","family":"Yang","sequence":"first","affiliation":[{"name":"College of Computer Science, Chongqing University, Chongqing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7648-5671","authenticated-orcid":false,"given":"Yantao","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science, Chongqing University, Chongqing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4425-9837","authenticated-orcid":false,"given":"Gang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Computer Science, William &amp; Mary, Williamsburg, VA, USA"}]}],"member":"320","published-online":{"date-parts":[[2023,4,18]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3469035"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.3390\/data3020011"},{"key":"e_1_3_1_4_2","first-page":"529","volume-title":"Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops\u201919)","author":"Arifoglu Damla","year":"2019","unstructured":"Damla Arifoglu and Abdelhamid Bouchachia. 2019. Abnormal behaviour detection for dementia sufferers via transfer learning and recursive auto-encoders. In Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops\u201919). IEEE, 529\u2013534."},{"key":"e_1_3_1_5_2","first-page":"214","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Arjovsky Martin","year":"2017","unstructured":"Martin Arjovsky, Soumith Chintala, and L\u00e9on Bottou. 2017. Wasserstein generative adversarial networks. In Proceedings of the International Conference on Machine Learning. PMLR, 214\u2013223."},{"issue":"2","key":"e_1_3_1_6_2","first-page":"1","article-title":"Design, implementation and validation of a novel open framework for agile development of mobile health applications","volume":"14","author":"Banos Oresti","year":"2015","unstructured":"Oresti Banos, Claudia Villalonga, Rafael Garcia, Alejandro Saez, Miguel Damas, Juan A. Holgado-Terriza, Sungyong Lee, Hector Pomares, and Ignacio Rojas. 2015. Design, implementation and validation of a novel open framework for agile development of mobile health applications. Biomed. Eng. Online 14, 2 (2015), 1\u201320. Retrieved from http:\/\/archive.ics.uci.edu\/ml\/datasets\/mhealth+dataset.","journal-title":"Biomed. Eng. Online"},{"key":"e_1_3_1_7_2","article-title":"Gan augmentation: Augmenting training data using generative adversarial networks","author":"Bowles Christopher","year":"2018","unstructured":"Christopher Bowles, Liang Chen, Ricardo Guerrero, Paul Bentley, Roger Gunn, Alexander Hammers, David Alexander Dickie, Maria Vald\u00e9s Hern\u00e1ndez, Joanna Wardlaw, and Daniel Rueckert. 2018. Gan augmentation: Augmenting training data using generative adversarial networks. Retrieved from https:\/\/arXiv:1810.10863.","journal-title":"Retrieved from https:\/\/arXiv:1810.10863"},{"issue":"4","key":"e_1_3_1_8_2","first-page":"57","article-title":"A comparative study of LSTM and phased LSTM for gait prediction","volume":"10","author":"Chen Qili","year":"2019","unstructured":"Qili Chen, Bofan Liang, and Jiuhe Wang. 2019. A comparative study of LSTM and phased LSTM for gait prediction. Int. J. Artificial. Intelli. App. 10, 4 (2019), 57\u201366.","journal-title":"Int. J. Artificial. Intelli. App."},{"key":"e_1_3_1_9_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Cheung Tsz-Him","year":"2020","unstructured":"Tsz-Him Cheung and Dit-Yan Yeung. 2020. Modals: Modality-agnostic automated data augmentation in the latent space. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_1_10_2","article-title":"Autoaugment: Learning augmentation policies from data","author":"Cubuk Ekin D.","year":"2018","unstructured":"Ekin D. Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V. Le. 2018. Autoaugment: Learning augmentation policies from data. Retrieved from https:\/\/arXiv:1805.09501.","journal-title":"Retrieved from https:\/\/arXiv:1805.09501"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00359"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2019.1911747"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-86890-1_10"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1093\/mnras\/stv632"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswax.2020.100033"},{"key":"e_1_3_1_16_2","doi-asserted-by":"crossref","first-page":"113075","DOI":"10.1016\/j.eswa.2019.113075","article-title":"Deep 1D-convnet for accurate Parkinson disease detection and severity prediction from gait","volume":"143","author":"Maachi Imanne El","year":"2020","unstructured":"Imanne El Maachi, Guillaume-Alexandre Bilodeau, and Wassim Bouachir. 2020. Deep 1D-convnet for accurate Parkinson disease detection and severity prediction from gait. Expert Syst. Appl. 143 (2020), 113075.","journal-title":"Expert Syst. Appl."},{"key":"e_1_3_1_17_2","first-page":"3029","volume-title":"Proceedings of the 37th International Conference on Machine Learning (Proceedings of Machine Learning Research)","volume":"119","author":"Farnia Farzan","year":"2020","unstructured":"Farzan Farnia and Asuman Ozdaglar. 2020. Do GANs always have Nash equilibria? In Proceedings of the 37th International Conference on Machine Learning (Proceedings of Machine Learning Research), Hal Daume III and Aarti Singh (Eds.), Vol. 119. PMLR, 3029\u20133039."},{"key":"e_1_3_1_18_2","first-page":"227","volume-title":"Proceedings of the International Conference on Computational Science and Computational Intelligence (CSCI\u201919)","author":"Fields Tonya","year":"2019","unstructured":"Tonya Fields, George Hsieh, and Jules Chenou. 2019. Mitigating drift in time-series data with noise augmentation. In Proceedings of the International Conference on Computational Science and Computational Intelligence (CSCI\u201919). IEEE, 227\u2013230."},{"issue":"1","key":"e_1_3_1_19_2","first-page":"1","article-title":"Artificial intelligence powers digital medicine","volume":"1","author":"Fogel Alexander L.","year":"2018","unstructured":"Alexander L. Fogel and Joseph C. Kvedar. 2018. Artificial intelligence powers digital medicine. NPJ Dig. Med. 1, 1 (2018), 1\u20134.","journal-title":"NPJ Dig. Med."},{"key":"e_1_3_1_20_2","article-title":"Adaptive weighting scheme for automatic time-series data augmentation","author":"Fons Elizabeth","year":"2021","unstructured":"Elizabeth Fons, Paula Dawson, Xiao-jun Zeng, John Keane, and Alexandros Iosifidis. 2021. Adaptive weighting scheme for automatic time-series data augmentation. Retrieved from https:\/\/arXiv:2102.08310.","journal-title":"Retrieved from https:\/\/arXiv:2102.08310"},{"key":"e_1_3_1_21_2","article-title":"Nips 2016 tutorial: Generative adversarial networks","author":"Goodfellow Ian","year":"2016","unstructured":"Ian Goodfellow. 2016. Nips 2016 tutorial: Generative adversarial networks. Retrieved from https:\/\/arXiv:1701.00160.","journal-title":"Retrieved from https:\/\/arXiv:1701.00160"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/3422622"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2005.06.042"},{"key":"e_1_3_1_24_2","article-title":"Improved training of wasserstein gans","author":"Gulrajani Ishaan","year":"2017","unstructured":"Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron Courville. 2017. Improved training of wasserstein gans. Retrieved from https:\/\/arXiv:1704.00028.","journal-title":"Retrieved from https:\/\/arXiv:1704.00028"},{"key":"e_1_3_1_25_2","article-title":"Mode collapse and regularity of optimal transportation maps","author":"Guo Yang","year":"2019","unstructured":"Yang Guo, Dongsheng An, Xin Qi, Zhongxuan Luo, Shing-Tung Yau, Xianfeng Gu, et\u00a0al. 2019. Mode collapse and regularity of optimal transportation maps. Retrieved from https:\/\/arXiv:1902.02934.","journal-title":"Retrieved from https:\/\/arXiv:1902.02934"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00745"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1145\/3386580"},{"key":"e_1_3_1_28_2","first-page":"21","volume-title":"Proceedings of the International Conference on Machine Learning Workshop on Deep Learning for Audio, Speech and Language (ICML\u201913)","volume":"117","author":"Jaitly Navdeep","year":"2013","unstructured":"Navdeep Jaitly and Geoffrey E. Hinton. 2013. Vocal tract length perturbation (VTLP) improves speech recognition. In Proceedings of the International Conference on Machine Learning Workshop on Deep Learning for Audio, Speech and Language (ICML\u201913), Vol. 117. 21."},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/3533708"},{"key":"e_1_3_1_30_2","first-page":"295","volume-title":"Proceedings of the IEEE 15th International Conference of System of Systems Engineering (SoSE\u201920)","author":"Kluwak Konrad","year":"2020","unstructured":"Konrad Kluwak and Teodor Ni\u017cy\u0144ski. 2020. Gait classification using LSTM networks for tagging system. In Proceedings of the IEEE 15th International Conference of System of Systems Engineering (SoSE\u201920). IEEE, 295\u2013300."},{"key":"e_1_3_1_31_2","unstructured":"Sayeri Lala Maha Shady Anastasiya Belyaeva and Molei Liu. 2018. Evaluation of mode collapse in generative adversarial networks. In Proceedings of the IEEE High Performance Extreme Computing Conference (HPEC\u201918) . IEEE 1\u20139."},{"key":"e_1_3_1_32_2","article-title":"Mmd gan: Towards deeper understanding of moment matching network","author":"Li Chun-Liang","year":"2017","unstructured":"Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, and Barnab\u00e1s P\u00f3czos. 2017. Mmd gan: Towards deeper understanding of moment matching network. Retrieved from https:\/\/arXiv:1705.08584.","journal-title":"Retrieved from https:\/\/arXiv:1705.08584."},{"issue":"1","key":"e_1_3_1_33_2","first-page":"628","article-title":"Using data augmentation in continuous authentication on smartphones","volume":"6","author":"Li Yantao","year":"2018","unstructured":"Yantao Li, Hailong Hu, and Gang Zhou. 2018. Using data augmentation in continuous authentication on smartphones. IEEE Internet Things J. 6, 1 (2018), 628\u2013640.","journal-title":"IEEE Internet Things J."},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2022.3186614"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2014.00094"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107187"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.5555\/2832249.2832411"},{"key":"e_1_3_1_38_2","first-page":"2535","volume-title":"Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC\u201918)","author":"Luo Yun","year":"2018","unstructured":"Yun Luo and Bao-Liang Lu. 2018. EEG data augmentation for emotion recognition using a conditional Wasserstein GAN. In Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC\u201918). IEEE, 2535\u20132538."},{"key":"e_1_3_1_39_2","article-title":"C-RNN-GAN: Continuous recurrent neural networks with adversarial training","author":"Mogren Olof","year":"2016","unstructured":"Olof Mogren. 2016. C-RNN-GAN: Continuous recurrent neural networks with adversarial training. Retrieved from https:\/\/arXiv:1611.09904.","journal-title":"Retrieved from https:\/\/arXiv:1611.09904."},{"key":"e_1_3_1_40_2","article-title":"Dynamic data augmentation with gating networks","author":"Oba Daisuke","year":"2021","unstructured":"Daisuke Oba, Shinnosuke Matsuo, and Brian Kenji Iwana. 2021. Dynamic data augmentation with gating networks. Retrieved from https:\/\/arXiv:2111.03253.","journal-title":"Retrieved from https:\/\/arXiv:2111.03253."},{"issue":"4","key":"e_1_3_1_41_2","doi-asserted-by":"crossref","first-page":"389","DOI":"10.3390\/electronics10040389","article-title":"Generating synthetic ECGs using GANs for anonymizing healthcare data","volume":"10","author":"Piacentino Esteban","year":"2021","unstructured":"Esteban Piacentino, Alvaro Guarner, and Cecilio Angulo. 2021. Generating synthetic ECGs using GANs for anonymizing healthcare data. Electronics 10, 4 (2021), 389.","journal-title":"Electronics"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2017.2657381"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-018-0058-9"},{"key":"e_1_3_1_44_2","first-page":"4570","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Shaham Tamar Rott","year":"2019","unstructured":"Tamar Rott Shaham, Tali Dekel, and Tomer Michaeli. 2019. Singan: Learning a generative model from a single natural image. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 4570\u20134580."},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.1145\/3136755.3136817"},{"issue":"86","key":"e_1_3_1_46_2","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten Laurens van der","year":"2008","unstructured":"Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 86 (2008), 2579\u20132605.","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_3_1_47_2","first-page":"1","volume-title":"Proceedings of the Computing in Cardiology (CinC\u201917)","author":"Xia Yong","year":"2017","unstructured":"Yong Xia, Naren Wulan, Kuanquan Wang, and Henggui Zhang. 2017. Atrial fibrillation detection using stationary wavelet transform and deep learning. In Proceedings of the Computing in Cardiology (CinC\u201917). IEEE, 1\u20134."},{"key":"e_1_3_1_48_2","unstructured":"Jinsung Yoon Daniel Jarrett and Mihaela Van der Schaar. 2019. Time-series generative adversarial networks. In Proceedings of Advances in Neural Information Processing Systems (NeurIPS\u201919) Vol. 32 1\u201311."},{"key":"e_1_3_1_49_2","first-page":"63","volume-title":"Proceedings of the 13th IASTED International Conference on Biomedical Engineering (BioMed\u201917)","author":"Zhang Chenshuang","year":"2017","unstructured":"Chenshuang Zhang, Guijin Wang, Jingwei Zhao, Pengfei Gao, Jianping Lin, and Huazhong Yang. 2017. Patient-specific ECG classification based on recurrent neural networks and clustering technique. In Proceedings of the 13th IASTED International Conference on Biomedical Engineering (BioMed\u201917). IEEE, 63\u201367."},{"key":"e_1_3_1_50_2","first-page":"012065","volume-title":"Journal of Physics: Conference Series","volume":"2037","author":"Zhao Zhen","year":"2021","unstructured":"Zhen Zhao, Ze Li, Fuxin Li, and Yang Liu. 2021. CNN-LSTM based traffic prediction using spatial-temporal features. In Journal of Physics: Conference Series, Vol. 2037. IOP Publishing, 012065."},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244"}],"container-title":["ACM Transactions on Computing for Healthcare"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583593","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3583593","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:37:54Z","timestamp":1750178274000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583593"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,18]]},"references-count":50,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,4,30]]}},"alternative-id":["10.1145\/3583593"],"URL":"https:\/\/doi.org\/10.1145\/3583593","relation":{},"ISSN":["2691-1957","2637-8051"],"issn-type":[{"value":"2691-1957","type":"print"},{"value":"2637-8051","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,18]]},"assertion":[{"value":"2022-07-07","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-01-26","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-04-18","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}