000048442 001__ 48442
000048442 005__ 20210121114458.0
000048442 0247_ $$2doi$$a10.1016/j.medengphy.2015.06.009
000048442 0248_ $$2sideral$$a94225
000048442 037__ $$aART-2015-94225
000048442 041__ $$aeng
000048442 100__ $$0(orcid)0000-0002-1561-0536$$aIgual, Raúl$$uUniversidad de Zaragoza
000048442 245__ $$aA comparison of public datasets for acceleration-based fall detection
000048442 260__ $$c2015
000048442 5060_ $$aAccess copy available to the general public$$fUnrestricted
000048442 5203_ $$aFalls are one of the leading causes of mortality among the older population, being the rapid detection of a fall a key factor to mitigate its main adverse health consequences. In this context, several authors have conducted studies on acceleration-based fall detection using external accelerometers or smartphones. The published detection rates are diverse, sometimes close to a perfect detector. This divergence may be explained by the difficulties in comparing different fall detection studies in a fair play since each study uses its own dataset obtained under different conditions. In this regard, several datasets have been made publicly available recently. This paper presents a comparison, to the best of our knowledge for the first time, of these public fall detection datasets in order to determine whether they have an influence on the declared performances. Using two different detection algorithms, the study shows that the performances of the fall detection techniques are affected, to a greater or lesser extent, by the specific datasets used to validate them. We have also found large differences in the generalization capability of a fall detector depending on the dataset used for training. In fact, the performance decreases dramatically when the algorithms are tested on a dataset different from the one used for training. Other characteristics of the datasets like the number of training samples also have an influence on the performance while algorithms seem less sensitive to the sampling frequency or the acceleration range.
000048442 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/TEC2013-50049-EXP
000048442 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000048442 590__ $$a1.619$$b2015
000048442 591__ $$aENGINEERING, BIOMEDICAL$$b45 / 76 = 0.592$$c2015$$dQ3$$eT2
000048442 592__ $$a0.794$$b2015
000048442 593__ $$aBiophysics$$c2015$$dQ2
000048442 593__ $$aBiomedical Engineering$$c2015$$dQ2
000048442 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000048442 700__ $$0(orcid)0000-0001-7671-7540$$aMedrano Carlos$$uUniversidad de Zaragoza
000048442 700__ $$0(orcid)0000-0001-7550-6688$$aPlaza, Inmaculada$$uUniversidad de Zaragoza
000048442 7102_ $$15008$$2785$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Tecnología Electrónica
000048442 7102_ $$15009$$2535$$aUniversidad de Zaragoza$$bDpto. Ingeniería Eléctrica$$cÁrea Ingeniería Eléctrica
000048442 773__ $$g37, 9 (2015), 870-878$$pMed. eng. phys.$$tMEDICAL ENGINEERING & PHYSICS$$x1350-4533
000048442 8564_ $$s813645$$uhttps://zaguan.unizar.es/record/48442/files/texto_completo.pdf$$yPostprint
000048442 8564_ $$s102177$$uhttps://zaguan.unizar.es/record/48442/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000048442 909CO $$ooai:zaguan.unizar.es:48442$$particulos$$pdriver
000048442 951__ $$a2021-01-21-10:49:23
000048442 980__ $$aARTICLE