46966 20200221144306.0 doi 10.3390/s16010117 sideral 93437 ART-2016-93437 eng (orcid)0000-0001-7671-7540 Medrano, C. Universidad de Zaragoza The effect of personalization on smartphone-based fall detectors 2016 Access copy available to the general public Unrestricted The 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. info:eu-repo/grantAgreement/ES/MINECO/TEC2013-50049-EXP info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 2.677 2016 INSTRUMENTS & INSTRUMENTATION 10 / 58 = 0.172 2016 Q1 T1 CHEMISTRY, ANALYTICAL 25 / 76 = 0.329 2016 Q2 T1 ELECTROCHEMISTRY 12 / 29 = 0.414 2016 Q2 T2 0.623 2016 Electrical and Electronic Engineering 2016 Q1 Analytical Chemistry 2016 Q2 Atomic and Molecular Physics, and Optics 2016 Q2 Medicine (miscellaneous) 2016 Q2 Instrumentation 2016 Q2 Biochemistry 2016 Q3 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion (orcid)0000-0001-7550-6688 Plaza, I. Universidad de Zaragoza (orcid)0000-0002-1561-0536 Igual, R. Universidad de Zaragoza Sánchez, Á. Castro, M. 5008 785 Universidad de Zaragoza Dpto. Ingeniería Electrón.Com. Área Tecnología Electrónica 5009 535 Universidad de Zaragoza Dpto. Ingeniería Eléctrica Área Ingeniería Eléctrica 16, 1 (2016), 117 Sensors Sensors (Switzerland) 1424-8220 822863 http://zaguan.unizar.es/record/46966/files/texto_completo.pdf Versión publicada 107204 http://zaguan.unizar.es/record/46966/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:46966 articulos driver 2020-02-21-13:35:12 ARTICLE