{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T06:07:28Z","timestamp":1730268448838,"version":"3.28.0"},"reference-count":17,"publisher":"IEEE","license":[{"start":{"date-parts":[[2020,8,1]],"date-time":"2020-08-01T00:00:00Z","timestamp":1596240000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,8,1]],"date-time":"2020-08-01T00:00:00Z","timestamp":1596240000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,8,1]],"date-time":"2020-08-01T00:00:00Z","timestamp":1596240000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,8]]},"DOI":"10.1109\/inista49547.2020.9194628","type":"proceedings-article","created":{"date-parts":[[2020,9,11]],"date-time":"2020-09-11T21:22:56Z","timestamp":1599859376000},"page":"1-7","source":"Crossref","is-referenced-by-count":1,"title":["Fall simulator for supporting supervised Machine Learning techniques in wearable devices"],"prefix":"10.1109","author":[{"given":"Armando","family":"Collado-Villaverde","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mario","family":"Cobos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pablo","family":"Munoz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria D.","family":"R-Moreno","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","first-page":"1940","article-title":"Customisable fall detection: a hybrid approach using physics based simulation and machine learning","volume":"54","author":"mastorakis","year":"2007","journal-title":"IEEE Transactions on Biomedical Engineering"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/IEMBS.2007.4352627"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/S0268-0033(03)00111-6"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.cogsys.2019.03.019"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1023\/B:JOHE.0000016717.22032.03"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1520\/JFS13997J"},{"key":"ref16","article-title":"Study of impact tolerance through free-fall investigations","author":"snyder","year":"1997","journal-title":"Insurance Institute for High-way Safety Washington D C USA"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.forsciint.2014.08.018"},{"key":"ref4","first-page":"379","article-title":"The biomechanical analysis of standing fall","volume":"51","author":"barbuceanu","year":"2006","journal-title":"Romanian J Phys"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TITB.2010.2087385"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46257-8_56"},{"key":"ref5","first-page":"29","article-title":"Detecci&#x00F3;n de ca&#x00ED;das mediante un aceler&#x00F3;metro de tres ejes ubicado en la mu&#x00F1;eca en personas de tercera edad","author":"collado-villaverde","year":"2016","journal-title":"Actas de la XVII Conferencia de la Asociaci&#x00F3;n Espa&#x00F1;ola para la Inteligencia Artificial"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/S0379-0738(98)00027-9"},{"key":"ref7","first-page":"1","article-title":"CARLA: An open urban driving simulator","author":"dosovitskiy","year":"0","journal-title":"Proceedings of the 1st Annual Conference on Robot Learning"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1556-4029.2010.01445.x"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.4271\/1999-01-0445"},{"key":"ref9","article-title":"Human fall detection methodologies: from machine learning using acted data to fall modelling using myoskeletal simulation","author":"mastorakis","year":"2018","journal-title":"PQDT - UK Irel"}],"event":{"name":"2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)","start":{"date-parts":[[2020,8,24]]},"location":"Novi Sad, Serbia","end":{"date-parts":[[2020,8,26]]}},"container-title":["2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9184152\/9194610\/09194628.pdf?arnumber=9194628","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T21:50:43Z","timestamp":1656453043000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9194628\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8]]},"references-count":17,"URL":"https:\/\/doi.org\/10.1109\/inista49547.2020.9194628","relation":{},"subject":[],"published":{"date-parts":[[2020,8]]}}}