Anomaly Detection in Activities of Daily Living with Linear Drift
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Other documents of the author: Belmonte-Fernández, Óscar; Caballer Miedes, Antonio; Chinellato, Eris; Montoliu Colás, Raul; Sansano-Sansano, Emilio; García-Vidal, Rubén
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Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/43662
comunitat-uji-handle3:10234/43643
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Title
Anomaly Detection in Activities of Daily Living with Linear DriftAuthor (s)
Date
2020-07-01Publisher
SpringerISSN
1866-9956; 1866-9964Bibliographic citation
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/s12559-020-09740-6Type
info:eu-repo/semantics/articlePublisher version
https://link.springer.com/article/10.1007/s12559-020-09740-6Version
info:eu-repo/semantics/submittedVersionSubject
Abstract
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. [-]
Is part of
Cognitive Computation, 2020Investigation project
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: PRX18/00123Rights
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