{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T02:08:48Z","timestamp":1743127728663,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030578015"},{"type":"electronic","value":"9783030578022"}],"license":[{"start":{"date-parts":[[2020,8,29]],"date-time":"2020-08-29T00:00:00Z","timestamp":1598659200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,8,29]],"date-time":"2020-08-29T00:00:00Z","timestamp":1598659200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-57802-2_54","type":"book-chapter","created":{"date-parts":[[2020,8,28]],"date-time":"2020-08-28T07:05:27Z","timestamp":1598598327000},"page":"563-570","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Time Series Data Augmentation and Dropout Roles in Deep Learning Applied to Fall Detection"],"prefix":"10.1007","author":[{"given":"Enol Garc\u00eda","family":"Gonz\u00e1lez","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9 Ram\u00f3n","family":"Villar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Enrique","family":"de la Cal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,8,29]]},"reference":[{"key":"54_CR1","doi-asserted-by":"crossref","unstructured":"Jahanjoo, A., Naderan, M., Rashti, M.J.: Detection and multi\u2013class classification of falling in elderly people by deep belief network algorithms. Ambient Intell. Human. Comput., 1\u201321 (2020)","DOI":"10.1007\/s12652-020-01690-z"},{"issue":"5","key":"54_CR2","doi-asserted-by":"publisher","first-page":"1350","DOI":"10.3390\/s18051350","volume":"18","author":"SB Khojasteh","year":"2018","unstructured":"Khojasteh, S.B., Villar, J.R., Chira, C., Su\u00e1rez, V.M.G., de la Cal, E.A.: Improving fall detection using an on-wrist wearable accelerometer. Sensors 18(5), 1350 (2018)","journal-title":"Sensors"},{"key":"54_CR3","doi-asserted-by":"crossref","unstructured":"Zhang, T., Wang, J., Xu, L., Liu, P.: Fall detection by wearable sensor and one-class SVM algorithm. In: Huang,\u00a0D.S., Li,\u00a0K., Irwin, G. (eds.) Intelligent Computing in Signal Processing and Pattern Recognition. Lecture Notes in Control and Information Systems, vol. 345, pp. 858\u2013863. Springer, Heidelberg (2006)","DOI":"10.1007\/11816515_104"},{"key":"54_CR4","first-page":"11","volume":"2015","author":"F Wu","year":"2015","unstructured":"Wu, F., Zhao, H., Zhao, Y., Zhong, H.: Development of a wearable-sensor-based fall detection system. Int. J. Telemedicine Appl. 2015, 11 (2015)","journal-title":"Int. J. Telemedicine Appl."},{"key":"54_CR5","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1016\/j.gaitpost.2006.09.012","volume":"26","author":"A Bourke","year":"2007","unstructured":"Bourke, A., O\u2019Brien, J., Lyons, G.: Evaluation of a threshold-based triaxial accelerometer fall detection algorithm. Gait Posture 26, 194\u2013199 (2007)","journal-title":"Gait Posture"},{"key":"54_CR6","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.proeng.2014.10.539","volume":"85","author":"YC Fang","year":"2014","unstructured":"Fang, Y.C., Dzeng, R.J.: A smartphone-based detection of fall portents for construction workers. Procedia Eng. 85, 147\u2013156 (2014)","journal-title":"Procedia Eng."},{"key":"54_CR7","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.autcon.2017.09.015","volume":"84","author":"YC Fang","year":"2017","unstructured":"Fang, Y.C., Dzeng, R.J.: Accelerometer-based fall-portent detection algorithm for construction tiling operation. Autom. Constr. 84, 214\u2013230 (2017)","journal-title":"Autom. Constr."},{"key":"54_CR8","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1155\/2015\/452078","volume":"2015","author":"QT Huynh","year":"2015","unstructured":"Huynh, Q.T., Nguyen, U.D., Irazabal, L.B., Ghassemian, N., Tran, B.Q.: Optimization of an accelerometer and gyroscope-based fall detection algorithm. J. Sens. 2015, 8 (2015)","journal-title":"J. Sens."},{"key":"54_CR9","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1016\/j.gaitpost.2008.01.003","volume":"28","author":"M Kangas","year":"2008","unstructured":"Kangas, M., Konttila, A., Lindgren, P., Winblad, I., J\u00e4msa\u00e4, T.: Comparison of low-complexity fall detection algorithms for body attached accelerometers. Gait Posture 28, 285\u2013291 (2008)","journal-title":"Gait Posture"},{"key":"54_CR10","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.procs.2017.01.188","volume":"105","author":"A Hakim","year":"2017","unstructured":"Hakim, A., Huq, M.S., Shanta, S., Ibrahim, B.: Smartphone based data mining for fall detection: analysis and design. Procedia Comput. Sci. 105, 46\u201351 (2017)","journal-title":"Procedia Comput. Sci."},{"issue":"4","key":"54_CR11","first-page":"453","volume":"8","author":"JR Villar","year":"2019","unstructured":"Villar, J.R., de la Cal, E.A., F\u00e1\u00f1ez, M., Su\u00e1rez, V.M.G., Sedano, J.: User-centered fall detection using supervised, on-line learning and transfer learning. Progress in AI 8(4), 453\u2013474 (2019)","journal-title":"Progress in AI"},{"key":"54_CR12","first-page":"219","volume":"2019","author":"M F\u00e1\u00f1ez","year":"2019","unstructured":"F\u00e1\u00f1ez, M., Villar, J.R., de la Cal, E.A., Su\u00e1rez, V.M.G., Sedano, J.: Feature clustering to improve fall detection: a preliminary study. SOCO 2019, 219\u2013228 (2019)","journal-title":"SOCO"},{"key":"54_CR13","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.maturitas.2017.03.317","volume":"100","author":"A Godfrey","year":"2017","unstructured":"Godfrey, A.: Wearables for independent living in older adults: gait and falls. Maturitas 100, 16\u201326 (2017)","journal-title":"Maturitas"},{"key":"54_CR14","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1186\/1475-925X-12-66","volume":"12","author":"R Igual","year":"2013","unstructured":"Igual, R., Medrano, C., Plaza, I.: Challenges, issues and trends in fall detection systems. BioMedical Eng. OnLine 12, 66 (2013)","journal-title":"BioMedical Eng. OnLine"},{"key":"54_CR15","doi-asserted-by":"crossref","unstructured":"Casilari-P\u2019erez, E., Lagos, F.G.: A comprehensive study on the use of artificial neural networks in wearable fall detection systems. Expert Syst. Appl. 138 (2019)","DOI":"10.1016\/j.eswa.2019.07.028"},{"key":"54_CR16","doi-asserted-by":"crossref","unstructured":"Wu, X., Cheng, L., Chu, C.H., Kim, J.: Using deep learning and smartphone for automatic detection of fall and daily activities. In: Lecture Notes in Computer Science, vol. 11924, pp. 61\u201374 (2019)","DOI":"10.1007\/978-3-030-34482-5_6"},{"key":"54_CR17","doi-asserted-by":"crossref","unstructured":"Casilari, E., Lora-Rivera, R., Garc\u00eda-Lagos, F.: A wearable fall detection system using deep learning. In: Advances and Trends in Artificial Intelligence, pp. 445\u2013456 (2019)","DOI":"10.1007\/978-3-030-22999-3_39"},{"key":"54_CR18","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.procs.2017.06.110","volume":"110","author":"E Casilari","year":"2017","unstructured":"Casilari, E.: Umafall: a multisensor dataset for the research on automatic fall detection. Procedia Comput. Sci. 110, 32\u201339 (2017)","journal-title":"Procedia Comput. Sci."}],"container-title":["Advances in Intelligent Systems and Computing","15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020)"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-57802-2_54","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,8,28]],"date-time":"2020-08-28T07:19:34Z","timestamp":1598599174000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-57802-2_54"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,29]]},"ISBN":["9783030578015","9783030578022"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-57802-2_54","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"type":"print","value":"2194-5357"},{"type":"electronic","value":"2194-5365"}],"subject":[],"published":{"date-parts":[[2020,8,29]]},"assertion":[{"value":"29 August 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SOCO","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Soft Computing Models in Industrial and Environmental Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Burgos","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2020","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":"socomoin2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2020.sococonference.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}