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Online Anomaly Detection for Smartphone-Based Multivariate Behavioral Time Series Data

Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
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Author to whom correspondence should be addressed.
Academic Editors: Sławomir Nowaczyk, Rita P. Ribeiro and Grzegorz Nalepa
Sensors 2022, 22(6), 2110; https://doi.org/10.3390/s22062110
Received: 4 February 2022 / Revised: 26 February 2022 / Accepted: 3 March 2022 / Published: 9 March 2022
(This article belongs to the Topic Data Science and Knowledge Discovery)
Smartphones can be used to collect granular behavioral data unobtrusively, over long time periods, in real-world settings. To detect aberrant behaviors in large volumes of passively collected smartphone data, we propose an online anomaly detection method using Hotelling’s T-squared test. The test statistic in our method was a weighted average, with more weight on the between-individual component when the amount of data available for the individual was limited and more weight on the within-individual component when the data were adequate. The algorithm took only an O(1) runtime in each update, and the required memory usage was fixed after a pre-specified number of updates. The performance of the proposed method, in terms of accuracy, sensitivity, and specificity, was consistently better than or equal to the offline method that it was built upon, depending on the sample size of the individual data. Future applications of our method include early detection of surgical complications during recovery and the possible prevention of the relapse of patients with serious mental illness. View Full-Text
Keywords: online learning; anomaly detection; Hotelling’s T-squared test; digital phenotyping online learning; anomaly detection; Hotelling’s T-squared test; digital phenotyping
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MDPI and ACS Style

Liu, G.; Onnela, J.-P. Online Anomaly Detection for Smartphone-Based Multivariate Behavioral Time Series Data. Sensors 2022, 22, 2110. https://doi.org/10.3390/s22062110

AMA Style

Liu G, Onnela J-P. Online Anomaly Detection for Smartphone-Based Multivariate Behavioral Time Series Data. Sensors. 2022; 22(6):2110. https://doi.org/10.3390/s22062110

Chicago/Turabian Style

Liu, Gang, and Jukka-Pekka Onnela. 2022. "Online Anomaly Detection for Smartphone-Based Multivariate Behavioral Time Series Data" Sensors 22, no. 6: 2110. https://doi.org/10.3390/s22062110

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