{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,30]],"date-time":"2025-08-30T16:59:54Z","timestamp":1756573194228,"version":"3.40.5"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031665370"},{"type":"electronic","value":"9783031665387"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-66538-7_21","type":"book-chapter","created":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T17:02:07Z","timestamp":1721926927000},"page":"204-209","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["The Impact of Synthetic Data on Fall Detection Application"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2777-2437","authenticated-orcid":false,"given":"Minakshi","family":"Debnath","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4908-445X","authenticated-orcid":false,"given":"Md Shahriar","family":"Kabir","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6968-6536","authenticated-orcid":false,"given":"Jianyuan","family":"Ni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5877-0230","authenticated-orcid":false,"given":"Anne Hee Hiong","family":"Ngu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,25]]},"reference":[{"key":"21_CR1","unstructured":"Enhancing fall detection: The role of synthetic data. https:\/\/drive.google.com\/file\/d\/1WLcxcjwg1d_t1i0T2RLr930Cpx-j64tt\/view"},{"key":"21_CR2","unstructured":"Smartfallmm watch accelerometer dataset. https:\/\/drive.google.com\/file\/d\/10tOrG7zgbLOgBJTFj0PDl3DYFjz7kkTO\/view"},{"key":"21_CR3","unstructured":"Sora. https:\/\/openai.com\/sora"},{"key":"21_CR4","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H. (eds.) NeurIPS, vol. 33, pp. 6840\u20136851. Curran Associates, Inc. (2020)"},{"issue":"7","key":"21_CR5","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0254841","volume":"16","author":"BK Iwana","year":"2021","unstructured":"Iwana, B.K., Uchida, S.: An empirical survey of data augmentation for time series classification with neural networks. PLoS ONE 16(7), e0254841 (2021)","journal-title":"PLoS ONE"},{"issue":"3","key":"21_CR6","first-page":"1","volume":"4","author":"H Kwon","year":"2020","unstructured":"Kwon, H., et al.: Imutube: Automatic extraction of virtual on-body accelerometry from video for human activity recognition. IMWUT 4(3), 1\u201329 (2020)","journal-title":"IMWUT"},{"key":"21_CR7","unstructured":"Li, X.: Mitigating data shortage in biomedical signal analysis: anbinvestigation intobtransfer learning and generative models. Ph.D. dissertation, Texas State University, Texas, June 2023"},{"key":"21_CR8","doi-asserted-by":"crossref","unstructured":"Liu, J., et al.: A graph attention spatio-temporal convolutional network for 3D human pose estimation in video. In: ICRA, pp. 3374\u20133380. IEEE (2021)","DOI":"10.1109\/ICRA48506.2021.9561605"},{"key":"21_CR9","doi-asserted-by":"crossref","unstructured":"Matos-Carvalho, J.P., Correia, S.D., Tomic, S.: Sensitivity analysis of lstm networks for fall detection wearable sensors. In: CIoT, pp. 112\u2013118 (2023)","DOI":"10.1109\/CIoT57267.2023.10084906"},{"issue":"1","key":"21_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3428666","volume":"2","author":"TR Mauldin","year":"2021","unstructured":"Mauldin, T.R., Ngu, A.H., Metsis, V., Canby, M.E.: Ensemble deep learning on wearables using small datasets. ACM Trans. Comput. Healthcare 2(1), 1\u201330 (2021)","journal-title":"ACM Trans. Comput. Healthcare"},{"issue":"10","key":"21_CR11","doi-asserted-by":"publisher","first-page":"1101","DOI":"10.3390\/app7101101","volume":"7","author":"D Micucci","year":"2017","unstructured":"Micucci, D., Mobilio, M., Napoletano, P.: UniMiB SHAR: a dataset for human activity recognition using acceleration data from smartphones. Appl. Sci. 7(10), 1101 (2017)","journal-title":"Appl. Sci."},{"key":"21_CR12","doi-asserted-by":"crossref","unstructured":"Ngu, A.H., Yasmin, A., Mahmud, T., Mahmood, A., Sheng, Q.Z.: Demo: P-fall: personalization pipeline for fall detection. In: Proceedings of the 8th ACM\/IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2023, pp. 173\u2013174. Association for Computing Machinery, New York (2024)","DOI":"10.1145\/3580252.3589412"},{"key":"21_CR13","unstructured":"Nikitin, A., Iannucci, L., Kaski, S.: TSGM \u2014 a flexible framework for synthetic time series generative modeling (2023)"},{"key":"21_CR14","unstructured":"Robinovitch, S.: Falls experienced by older adult residents in long-term care homes (2018)"},{"key":"21_CR15","doi-asserted-by":"crossref","unstructured":"Zebiah, S.S. et al.: Human fall detection using machine learning and deep learning techniques: a survey. In: ICSPC, pp. 253\u2013257 (2023)","DOI":"10.1109\/ICSPC57692.2023.10125648"},{"key":"21_CR16","doi-asserted-by":"crossref","unstructured":"Um, T.T., et al.: Data augmentation of wearable sensor data for Parkinson\u2019s disease monitoring using convolutional neural networks. In: ICMI, pp. 216\u2013220. ACM, New York (2017)","DOI":"10.1145\/3136755.3136817"},{"issue":"4","key":"21_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3626235","volume":"56","author":"L Yang","year":"2023","unstructured":"Yang, L., et al.: Diffusion models: a comprehensive survey of methods and applications. ACM Comput. Surv. 56(4), 1\u201339 (2023)","journal-title":"ACM Comput. Surv."},{"key":"21_CR18","doi-asserted-by":"publisher","first-page":"692865","DOI":"10.3389\/fnagi.2021.692865","volume":"13","author":"Y Xiaoqun","year":"2021","unstructured":"Xiaoqun, Y., Jang, J., Xiong, S.: A large-scale open motion dataset (kfall) and benchmark algorithms for detecting pre-impact fall of the elderly using wearable inertial sensors. Front. Aging Neurosci. 13, 692865 (2021)","journal-title":"Front. Aging Neurosci."}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-66538-7_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,24]],"date-time":"2024-11-24T21:03:14Z","timestamp":1732482194000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-66538-7_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031665370","9783031665387"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-66538-7_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"25 July 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIME","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence in Medicine","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Salt Lake City, UT","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aime2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aime24.aimedicine.info\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}