{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T10:18:37Z","timestamp":1776334717729,"version":"3.51.2"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031486418","type":"print"},{"value":"9783031486425","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-48642-5_16","type":"book-chapter","created":{"date-parts":[[2023,11,25]],"date-time":"2023-11-25T09:02:16Z","timestamp":1700902936000},"page":"167-172","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Using Synthetic Data to Improve the Accuracy of Human Activity Recognition"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3772-2299","authenticated-orcid":false,"given":"Majid","family":"Liaquat","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0882-7902","authenticated-orcid":false,"given":"Chris","family":"Nugent","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2368-7354","authenticated-orcid":false,"given":"Ian","family":"Cleland","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,26]]},"reference":[{"key":"16_CR1","doi-asserted-by":"crossref","unstructured":"Pires, I.M., Hussain, F., Marques, G., Garcia, N.M.: Comparison of machine learning techniques for the identification of human activities from inertial sensors available in a mobile device after the application of data imputation techniques. Comput. Biol. Med. 135, 104638 (2021)","DOI":"10.1016\/j.compbiomed.2021.104638"},{"key":"16_CR2","doi-asserted-by":"crossref","unstructured":"Murtaza, H., Ahmed,  M., Khan,  N.F., Murtaza, G., Zafar, S., Bano, A.: Synthetic data generation: state of the art in health care domain. Comput. Sci. Rev.  48, 100546 (2023)","DOI":"10.1016\/j.cosrev.2023.100546"},{"key":"16_CR3","doi-asserted-by":"crossref","unstructured":"DeOliveira, J., Gerych,  W., Koshkarova,  A., Rundensteiner, E., Agu, E.: HAR-CTGAN: a mobile sensor data generation tool for human activity recognition. In: IEEE International Conference on Big Data (Big Data), pp. 5233\u20135242 (2022)","DOI":"10.1109\/BigData55660.2022.10020848"},{"key":"16_CR4","doi-asserted-by":"crossref","unstructured":"Dahmen, J., Cook, D.: SynSys: a synthetic data generation system for healthcare applications. Sensors (Basel), 19(5). 1181 (2019)","DOI":"10.3390\/s19051181"},{"key":"16_CR5","doi-asserted-by":"crossref","unstructured":"Rajendran, M., Tan, C.T., Atmosukarto, I., Ng,  A.B., See, S.: SynDa: a novel synthetic data generation pipeline for activity recognition. In: IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), pp. 373\u2013377 (2022)","DOI":"10.1109\/ISMAR-Adjunct57072.2022.00081"},{"key":"16_CR6","doi-asserted-by":"crossref","unstructured":"Qu, L., Wang, Y., Yang, T., Sun, Y.: Human activity recognition based on WRGAN-GP-synthesized micro-doppler spectrograms. IEEE Sens. J. 22(9), 8960\u20138973 (2022)","DOI":"10.1109\/JSEN.2022.3164152"},{"key":"16_CR7","doi-asserted-by":"crossref","unstructured":"Jimale, A.O., Mohd Noor, M.H.: Fully connected generative adversarial network for human activity recognition. IEEE Access. 10, 100257\u2013100266 (2022)","DOI":"10.1109\/ACCESS.2022.3206952"},{"key":"16_CR8","doi-asserted-by":"crossref","unstructured":"Anjum, F., Alam, S., Bahadur, E.H., Muhammad Masum, A.K., Rahman, M.Z.: Deep learning for depression symptomatic activity recognition. In: 2022 International Conference on Innovations in Science, Engineering and Technology (ICISET), pp. 510\u2013515 (2022)","DOI":"10.1109\/ICISET54810.2022.9775922"},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"Anowar, F., Sadaoui,  S., Selim, B.: Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE). Comput. Sci. Rev. 40(100378), 100378 (2021)","DOI":"10.1016\/j.cosrev.2021.100378"},{"key":"16_CR10","doi-asserted-by":"crossref","unstructured":"Banos, O., et al.: mHealthDroid: a novel framework for agile development of mobile health applications. In: Pecchia, L., Chen, L.L., Nugent, C., Bravo, J. (eds.) Ambient Assisted Living and Daily Activities. \nIWAAL 2014. LNCS, vol. 8868, 91\u201398. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-13105-4_14","DOI":"10.1007\/978-3-319-13105-4_14"},{"key":"16_CR11","doi-asserted-by":"crossref","unstructured":"Banos, O., et al.: Design, implementation and validation of a novel open framework for agile development of mobile health applications. Biomed. Eng. Online, 14(Suppl 2), p. S6 (2015)","DOI":"10.1186\/1475-925X-14-S2-S6"},{"key":"16_CR12","unstructured":"UCI Machine Learning Repository: MHEALTH Dataset Data Set. http:\/\/archive.ics.uci.edu\/ml\/datasets\/mhealth+dataset"},{"key":"16_CR13","unstructured":"sklearn.feature_selection.mutual_info_classif. https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.feature_selection.mutual_info_classif.html"},{"key":"16_CR14","unstructured":"Zhang, K., Patki, N., Veeramachaneni, K.: Sequential models in the synthetic data vault. arXiv preprint arXiv:2207.14406 (2022)"},{"key":"16_CR15","unstructured":"Yoon, J., Jarrett, D., van der Schaar, M.: Time-series Generative Adversarial Networks. In: Advances in neural information processing systems, vol. 32 (2019)"},{"key":"16_CR16","unstructured":"Kalyan, V., Xu, L., Skoularidou, M., Cuesta-Infante, A.: Modeling tabular data using conditional GAN. In: Advances in neural information processing systems, vol. 32 (2019)"}],"container-title":["Lecture Notes in Networks and Systems","Proceedings of the 15th International Conference on Ubiquitous Computing &amp; Ambient Intelligence (UCAmI 2023)"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-48642-5_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,25]],"date-time":"2023-11-25T09:14:06Z","timestamp":1700903646000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-48642-5_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031486418","9783031486425"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-48642-5_16","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"value":"2367-3370","type":"print"},{"value":"2367-3389","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"26 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"UCAmI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Ubiquitous Computing and Ambient Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Riviera Maya","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexico","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ucami2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ucami.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}