{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T01:51:46Z","timestamp":1777513906628,"version":"3.51.4"},"publisher-location":"Basel Switzerland","reference-count":27,"publisher":"MDPI","license":[{"start":{"date-parts":[[2018,10,22]],"date-time":"2018-10-22T00:00:00Z","timestamp":1540166400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Universidad Panamericana","award":["UP-CI-2017-ING-MX-02"],"award-info":[{"award-number":["UP-CI-2017-ING-MX-02"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"DOI":"10.3390\/proceedings2191237","type":"proceedings-article","created":{"date-parts":[[2018,10,23]],"date-time":"2018-10-23T08:43:36Z","timestamp":1540284216000},"page":"1237","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Multimodal Database for Human Activity Recognition and Fall Detection"],"prefix":"10.3390","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9038-7821","authenticated-orcid":false,"given":"Lourdes","family":"Mart\u00ednez-Villase\u00f1or","sequence":"first","affiliation":[{"name":"Facultad de Ingenier\u00eda, Universidad Panamericana, Augusto Rodin 498, 03920 Ciudad de M\u00e9xico, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6559-7501","authenticated-orcid":false,"given":"Hiram","family":"Ponce","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda, Universidad Panamericana, Augusto Rodin 498, 03920 Ciudad de M\u00e9xico, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ricardo Abel","family":"Espinosa-Loera","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda, Universidad Panamericana, Josemar\u00eda Escriv\u00e1 de Balaguer 101, 20290 Aguascalientes, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,10,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1093\/ageing\/afw129","article-title":"Prevalence and risk factors for falls in older men and women: The English Longitudinal Study of Ageing","volume":"45","author":"Gale","year":"2016","journal-title":"Age Ageing"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"29","DOI":"10.4103\/jfcm.JFCM_48_17","article-title":"Falls among elderly and its relation with their health problems and surrounding environmental factors in Riyadh","volume":"25","author":"Alshammari","year":"2018","journal-title":"J. Fam. Commun. Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1186\/1475-925X-12-66","article-title":"Challenges, issues and trends in fall detection systems","volume":"12","author":"Igual","year":"2013","journal-title":"Biomed. Eng. Online"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Noury, N., Fleury, A., Rumeau, P., Bourke, A.K., Laighin, G.O., Rialle, V., and Lundy, J.E. (2007, January 22\u201326). Fall detection-principles and methods. Proceedings of the 29th Annual International Conference of the Engineering in Medicine and Biology Society, Lyon, France.","DOI":"10.1109\/IEMBS.2007.4352627"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.neucom.2011.09.037","article-title":"A survey on fall detection: Principles and approaches","volume":"100","author":"Mubashir","year":"2013","journal-title":"Neurocomputing"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1449","DOI":"10.1109\/JPROC.2015.2460697","article-title":"Multimodal data fusion: An overview of methods, challenges, and prospects","volume":"103","author":"Lahat","year":"2015","journal-title":"Proc. IEEE"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Koshmak, G., Loutfi, A., and Linden, M. (2016). Challenges and issues in multisensor fusion approach for fall detection. J. Sens.","DOI":"10.1155\/2016\/6931789"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"870","DOI":"10.1016\/j.medengphy.2015.06.009","article-title":"A comparison of public datasets for acceleration-based fall detection","volume":"37","author":"Igual","year":"2015","journal-title":"Med. Eng. Phys."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Casilari, E., Santoyo-Ram\u00f3n, J.A., and Cano-Garc\u00eda, J. (2017). Analysis of public datasets for wearable fall detection systems. Sensors, 17.","DOI":"10.3390\/s17071513"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Frank, K., Nadales, V., Robertson, M.J., and Pfeifer, T. (2010, January 26\u201329). Bayesian recognition of motion related activities with inertial sensors. Proceedings of the 12th ACM International Conference Adjunct Papers on Ubiquitous Computing-Adjunct, Copenhagen, Denmark.","DOI":"10.1145\/1864431.1864480"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"44","DOI":"10.4018\/ijmstr.2014010103","article-title":"The mobifall dataset: Fall detection and classification with a smartphone","volume":"2","author":"Vavoulas","year":"2014","journal-title":"Int. J. Monit. Surveillance Technol. Res."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Medrano, C., Igual, R., Plaza, I., and Castro, M. (2014). Detecting falls as novelties in acceleration patterns acquired with smartphones. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0094811"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Vilarinho, T., Farshchian, B., Bajer, D., Dahl, O., Egge, I., Hegdal, S.S., L\u00f8nes, A., Slettevold, J.N., and Weggersen, S.M. (2015, January 26\u201328). A combined smartphone and smartwatch fall detection system. Proceedings of the 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT\/IUCC\/DASC\/PICOM), Liverpool, UK,.","DOI":"10.1109\/CIT\/IUCC\/DASC\/PICOM.2015.216"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Sucerquia, A., L\u00f3pez, J.D., and Vargas-Bonilla, J.F. (2017). SisFall: A fall and movement dataset. Sensors, 17.","DOI":"10.3390\/s17010198"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kepski, M., Kwolek, B., and Austvoll, I. (2012). Fuzzy inference-based reliable fall detection using kinect and accelerometer. Artificial Intelligence and Soft Computing of Lecture Notes in Computer Science, Springer.","DOI":"10.1007\/978-3-642-29347-4_31"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1016\/j.neucom.2015.05.061","article-title":"Improving fall detection by the use of depth sensor and accelerometer","volume":"168","author":"Kwolek","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ofli, F., Chaudhry, R., Kurillo, G., Vidal, R., and Bajcsy, R. (2013, January 15\u201317). Berkeley MHAD: A comprehensive multimodal human action database. Proceedings of the 2013 IEEE Workshop on Applications of Computer Vision (WACV), Tampa, FL, USA.","DOI":"10.1109\/WACV.2013.6474999"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.inffus.2011.08.001","article-title":"Multisensor data fusion: A review of the state-of-the-art","volume":"14","author":"Khaleghi","year":"2013","journal-title":"Inf. Fusion"},{"key":"ref_19","unstructured":"(2018, May 10). DLRdataset. Available online: www.dlr.de\/kn\/en\/Portaldata\/27\/Resources\/dokumente\/04_abteilungen_fs\/kooperative_systeme\/high_precision_reference_data\/Activity_DataSet.zip."},{"key":"ref_20","unstructured":"(2016, January 10). MobiFalldataset. Available online: http:\/\/www.bmi.teicrete.gr\/index.php\/research\/mobifall."},{"key":"ref_21","unstructured":"(2018, May 10). EduQTech, tFall: EduQTechdataset. Published July 2013. Available online: http:\/\/eduqtech.unizar.es\/fall-adl-data\/."},{"key":"ref_22","unstructured":"(2018, May 10). Sistemas Embebidos e Inteligencia Computacional, SISTEMIC: SisFall Dataset. Available online: http:\/\/sistemic.udea.edu.co\/investigacion\/proyectos\/english-falls\/?lang=en."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1475","DOI":"10.1109\/TITB.2010.2051956","article-title":"Detection of falls among the elderly by a floor sensor using the electric near field","volume":"14","author":"Rimminen","year":"2010","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_24","unstructured":"Tzeng, H.W., Chen, M.Y., and Chen, J.Y. (2010, January 1\u20133). Design of fall detection system with floor pressure and infrared image. Proceedings of the 2010 International Conference on System Science and Engineering (ICSSE), Taipei, Taiwan."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Mastorakis, G., and Makris, D. (2012). Fall detection system using Kinect\u2019s infrared sensor. J. Real-Time Image Process., 1\u201312.","DOI":"10.1007\/s11554-012-0246-9"},{"key":"ref_26","unstructured":"(2018, May 10). URFD University of Kseszow Fall Detection Dataset. Available online: http:\/\/fenix.univ.rzeszow.pl\/mkepski\/ds\/uf.html."},{"key":"ref_27","unstructured":"(2018, May 10). Teleimmersion Lab, University of California, Berkeley, 2013, Berkeley Multimodal Human Action Database (MHAD). Available online: http:\/\/tele-immersion.citris-uc.org\/berkeley_mhad."}],"event":{"name":"The International Conference on Ubiquitous Computing and Ambient \u202aIntelligence\u202c\u202c","acronym":"UCAmI 2018"},"container-title":["UCAmI 2018"],"original-title":[],"link":[{"URL":"https:\/\/www.mdpi.com\/2504-3900\/2\/19\/1237\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:25:23Z","timestamp":1760196323000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-3900\/2\/19\/1237"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,10,22]]},"references-count":27,"alternative-id":["proceedings2191237"],"URL":"https:\/\/doi.org\/10.3390\/proceedings2191237","relation":{},"subject":[],"published":{"date-parts":[[2018,10,22]]}}}