Resumen: 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. Idioma: Inglés DOI: 10.1007/s11517-017-1632-z Año: 2017 Publicado en: MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 55, 10 (2017), 1849–1858 ISSN: 0140-0118 Factor impacto JCR: 1.971 (2017) Categ. JCR: MATHEMATICAL & COMPUTATIONAL BIOLOGY rank: 19 / 59 = 0.322 (2017) - Q2 - T1 Categ. JCR: ENGINEERING, BIOMEDICAL rank: 41 / 77 = 0.532 (2017) - Q3 - T2 Categ. JCR: MEDICAL INFORMATICS rank: 14 / 25 = 0.56 (2017) - Q3 - T2 Categ. JCR: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS rank: 52 / 105 = 0.495 (2017) - Q2 - T2 Factor impacto SCIMAGO: 0.661 - Computer Science Applications (Q2) - Biomedical Engineering (Q2)