title: Combining novelty detectors to improve accelerometer-based fall detection.
abstract: Research on body-worn sensors has shown how they can be used for the detection of falls in the elderly, which is a relevant health problem. However, most systems are trained with simulated falls, which differ from those of the target population. In this paper, we tackle the problem of fall detection using a combination of novelty detectors. A novelty detector can be trained only with activities of daily life (ADL), which are true movements recorded in real life. In addition, they allow adapting the system to new users, by recording new movements and retraining the system. The combination of several detectors and features enhances performance. The proposed approach has been compared with a traditional supervised algorithm, a support vector machine, which is trained with both falls and ADL. The combination of novelty detectors shows better performance in a typical cross-validation test and in an experiment that mimics the effect of personalizing the classifiers. The results indicate that it is possible to build a reliable fall detector based only on ADL.
authors.affiliation: Instituto de Investigación Sanitaria Aragón / Department of Electronics Engineering and Communications, Department of Electrical Engineering / EduQTech, EUPT, University of Zaragoza, c/Atarazana 2, 44003, Teruel, Spain.
authors.forename: Carlos
authors.initials: C
authors.lastname: Medrano
authors.affiliation: Instituto de Investigación Sanitaria Aragón / Department of Electronics Engineering and Communications, Department of Electrical Engineering / EduQTech, EUPT, University of Zaragoza, c/Atarazana 2, 44003, Teruel, Spain. rigual@unizar.es.
authors.forename: Raúl
authors.identifier: http://orcid.org/0000-0002-1561-0536
authors.initials: R
authors.lastname: Igual
authors.affiliation: Instituto de Investigación Sanitaria Aragón / Department of Electronics Engineering and Communications, Department of Electrical Engineering / EduQTech, EUPT, University of Zaragoza, c/Atarazana 2, 44003, Teruel, Spain.
authors.forename: Iván
authors.initials: I
authors.lastname: García-Magariño
authors.affiliation: Instituto de Investigación Sanitaria Aragón / Department of Electronics Engineering and Communications, Department of Electrical Engineering / EduQTech, EUPT, University of Zaragoza, c/Atarazana 2, 44003, Teruel, Spain.
authors.forename: Inmaculada
authors.initials: I
authors.lastname: Plaza
authors.affiliation: Department of Electronics Engineering and Communications/ CeNITEQ, EUPT, University of Zaragoza, c/Atarazana 2, 44003, Teruel, Spain.
authors.forename: Guillermo
authors.initials: G
authors.lastname: Azuara
mesh_terms: D000054:Acceleration; D061725:Accelerometry / Q000379:methods*; D000058:Accidental Falls / Q000517:prevention & control*; D000203:Activities of Daily Living; D000368:Aged; D000465:Algorithms; D006801:Humans; D018670:Monitoring, Ambulatory / Q000379:methods; D009068:Movement / Q000502:physiology; D060388:Support Vector Machine
keywords: Fall detection; Mobility; Pattern recognition; eHealth