Anomaly Detection in Activities of Daily Living with Linear Drift
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Otros documentos de la autoría: Belmonte-Fernández, Óscar; Caballer Miedes, Antonio; Chinellato, Eris; Montoliu Colás, Raul; Sansano-Sansano, Emilio; García-Vidal, Rubén
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/43662
comunitat-uji-handle3:10234/43643
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INVESTIGACIONMetadatos
Título
Anomaly Detection in Activities of Daily Living with Linear DriftAutoría
Fecha de publicación
2020-07-01Editor
SpringerISSN
1866-9956; 1866-9964Cita bibliográfica
Belmonte-Fernández, Ó., Caballer-Miedes, A., Chinellato, E. et al. Anomaly Detection in Activities of Daily Living with Linear Drift. Cogn Comput (2020). https://doi.org/10.1007/s12559-020-09740-6Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://link.springer.com/article/10.1007/s12559-020-09740-6Versión
info:eu-repo/semantics/submittedVersionPalabras clave / Materias
Resumen
Anomalyq detection in Activities of Daily Living (ADL) plays an important role in e-health applications. An abrupt change in the ADL performed by a subject might indicate that she/he needs some help. Another important ... [+]
Anomalyq detection in Activities of Daily Living (ADL) plays an important role in e-health applications. An abrupt change in the ADL performed by a subject might indicate that she/he needs some help. Another important issue related with e-health applications is the case where the change in ADL undergoes a linear drift, which occurs in cognitive decline, Alzheimer’s disease or dementia. This work presents a novel method for detecting a linear drift in ADL modelled as circular normal distributions. The method is based on techniques commonly used in Statistical Process Control and, through the selection of a convenient threshold, is able to detect and estimate the change point in time when a linear drift started. Public datasets have been used to assess whether ADL can be modelled by a mixture of circular normal distributions. Exhaustive experimentation was performed on simulated data to assess the validity of the change detection algorithm, the results showing that the difference between the real change point and the estimated change point was 4.90−1.98+3.17 days on average. ADL can be modelled using a mixture of circular normal distributions. A new method to detect anomalies following a linear drift is presented. Exhaustive experiments showed that this method is able to estimate the change point in time for processes following a linear drift. [-]
Publicado en
Cognitive Computation, 2020Proyecto de investigación
Spanish Ministry of Science, Innovation and Universities through the “Retos investigación” programme: RTI2018-095168-B-C53; Universitat Jaume I “Pla de promoció de la investigació 2017” programme: UJI-B2017-45; Spanish Ministry of Science, Innovation and Universities: PRX18/00123Derechos de acceso
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