000046966 001__ 46966 000046966 005__ 20200221144306.0 000046966 0247_ $$2doi$$a10.3390/s16010117 000046966 0248_ $$2sideral$$a93437 000046966 037__ $$aART-2016-93437 000046966 041__ $$aeng 000046966 100__ $$0(orcid)0000-0001-7671-7540$$aMedrano, C.$$uUniversidad de Zaragoza 000046966 245__ $$aThe effect of personalization on smartphone-based fall detectors 000046966 260__ $$c2016 000046966 5060_ $$aAccess copy available to the general public$$fUnrestricted 000046966 5203_ $$aThe risk of falling is high among different groups of people, such as older people, individuals with Parkinson''s disease or patients in neuro-rehabilitation units. Developing robust fall detectors is important for acting promptly in case of a fall. Therefore, in this study we propose to personalize smartphone-based detectors to boost their performance as compared to a non-personalized system. Four algorithms were investigated using a public dataset: three novelty detection algorithms—Nearest Neighbor (NN), Local Outlier Factor (LOF) and One-Class Support Vector Machine (OneClass-SVM)—and a traditional supervised algorithm, Support Vector Machine (SVM). The effect of personalization was studied for each subject by considering two different training conditions: data coming only from that subject or data coming from the remaining subjects. The area under the receiver operating characteristic curve (AUC) was selected as the primary figure of merit. The results show that there is a general trend towards the increase in performance by personalizing the detector, but the effect depends on the individual being considered. A personalized NN can reach the performance of a non-personalized SVM (average AUC of 0.9861 and 0.9795, respectively), which is remarkable since NN only uses activities of daily living for training. 000046966 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/TEC2013-50049-EXP 000046966 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/ 000046966 590__ $$a2.677$$b2016 000046966 591__ $$aINSTRUMENTS & INSTRUMENTATION$$b10 / 58 = 0.172$$c2016$$dQ1$$eT1 000046966 591__ $$aCHEMISTRY, ANALYTICAL$$b25 / 76 = 0.329$$c2016$$dQ2$$eT1 000046966 591__ $$aELECTROCHEMISTRY$$b12 / 29 = 0.414$$c2016$$dQ2$$eT2 000046966 592__ $$a0.623$$b2016 000046966 593__ $$aElectrical and Electronic Engineering$$c2016$$dQ1 000046966 593__ $$aAnalytical Chemistry$$c2016$$dQ2 000046966 593__ $$aAtomic and Molecular Physics, and Optics$$c2016$$dQ2 000046966 593__ $$aMedicine (miscellaneous)$$c2016$$dQ2 000046966 593__ $$aInstrumentation$$c2016$$dQ2 000046966 593__ $$aBiochemistry$$c2016$$dQ3 000046966 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000046966 700__ $$0(orcid)0000-0001-7550-6688$$aPlaza, I.$$uUniversidad de Zaragoza 000046966 700__ $$0(orcid)0000-0002-1561-0536$$aIgual, R.$$uUniversidad de Zaragoza 000046966 700__ $$aSánchez, Á. 000046966 700__ $$aCastro, M. 000046966 7102_ $$15008$$2785$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Tecnología Electrónica 000046966 7102_ $$15009$$2535$$aUniversidad de Zaragoza$$bDpto. Ingeniería Eléctrica$$cÁrea Ingeniería Eléctrica 000046966 773__ $$g16, 1 (2016), 117$$pSensors$$tSensors (Switzerland)$$x1424-8220 000046966 8564_ $$s822863$$uhttps://zaguan.unizar.es/record/46966/files/texto_completo.pdf$$yVersión publicada 000046966 8564_ $$s107204$$uhttps://zaguan.unizar.es/record/46966/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000046966 909CO $$ooai:zaguan.unizar.es:46966$$particulos$$pdriver 000046966 951__ $$a2020-02-21-13:35:12 000046966 980__ $$aARTICLE