000057859 001__ 57859
000057859 005__ 20180117090614.0
000057859 0247_ $$2doi$$a10.1371/journal.pone.0094811
000057859 0248_ $$2sideral$$a88504
000057859 037__ $$aART-2014-88504
000057859 041__ $$aeng
000057859 100__ $$0(orcid)0000-0001-7671-7540$$aMedrano, C.$$uUniversidad de Zaragoza
000057859 245__ $$aDetecting Falls as Novelties in Acceleration Patterns Acquired with Smartphones
000057859 260__ $$c2014
000057859 5060_ $$aAccess copy available to the general public$$fUnrestricted
000057859 5203_ $$aDespite being a major public health problem, falls in the elderly cannot be detected efficiently yet. Many studies have used acceleration as the main input to discriminate between falls and activities of daily living (ADL). In recent years, there has been an increasing interest in using smartphones for fall detection. The most promising results have been obtained by supervised Machine Learning algorithms. However, a drawback of these approaches is that they rely on falls simulated by young or mature people, which might not represent every possible fall situation and might be different from older people's falls. Thus, we propose to tackle the problem of fall detection by applying a kind of novelty detection methods which rely only on true ADL. In this way, a fall is any abnormal movement with respect to ADL. A system based on these methods could easily adapt itself to new situations since new ADL could be recorded continuously and the system could be re-trained on the fly. The goal of this work is to explore the use of such novelty detectors by selecting one of them and by comparing it with a state-of-the-art traditional supervised method under different conditions. The data sets we have collected were recorded with smartphones. Ten volunteers simulated eight type of falls, whereas ADL were recorded while they carried the phone in their real life. Even though we have not collected data from the elderly, the data sets were suitable to check the adaptability of novelty detectors. They have been made publicly available to improve the reproducibility of our results. We have studied several novelty detection methods, selecting the nearest neighbour-based technique (NN) as the most suitable. Then, we have compared NN with the Support Vector Machine (SVM). In most situations a generic SVM outperformed an adapted NN.
000057859 536__ $$9info:eu-repo/grantAgreement/ES/DGA/CTPP11-12 sMartxan basic
000057859 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000057859 590__ $$a3.234$$b2014
000057859 591__ $$aMULTIDISCIPLINARY SCIENCES$$b9 / 57 = 0.158$$c2014$$dQ1$$eT1
000057859 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000057859 700__ $$aIgual, R.
000057859 700__ $$0(orcid)0000-0001-7550-6688$$aPlaza, I.$$uUniversidad de Zaragoza
000057859 700__ $$aCastro, M.
000057859 7102_ $$15008$$2785$$aUniversidad de Zaragoza$$bDepartamento de Ingeniería Electrónica y Comunicaciones$$cTecnología Electrónica
000057859 773__ $$g9, 4 (2014), e94811 [9 pp]$$pPLoS One$$tPLoS One$$x1932-6203
000057859 8564_ $$s1513506$$uhttp://zaguan.unizar.es/record/57859/files/texto_completo.pdf$$yVersión publicada
000057859 8564_ $$s125815$$uhttp://zaguan.unizar.es/record/57859/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000057859 909CO $$ooai:zaguan.unizar.es:57859$$particulos$$pdriver
000057859 951__ $$a2018-01-17-08:58:43
000057859 980__ $$aARTICLE