Abstract
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 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.
Highlights
Detection for Smartphone-Digital phenotyping has been defined as “the moment-by-moment quantification of the individual-level human phenotype in situ using data from personal digital devices,”in particular smartphones [1]
Let yij = μij + sij + eij be the value of the ith feature on day j of follow-up, where eij is the error component and μij is the trend component estimated from a weighted average of the previous observed feature values yi,j−1, yi,j−2, . . ., with more weight given to observations closer in time
Our findings showed that the value of the within-individual component of the test statistic approximates the offline test statistic, but is faster to compute
Summary
Detection for Smartphone-Digital phenotyping has been defined as “the moment-by-moment quantification of the individual-level human phenotype in situ using data from personal digital devices,”in particular smartphones [1]. Collected smartphone behavioral data [2] consist of data from sensors, such as the built-in Global Positioning System (GPS) and accelerometer, as well as phone usage data, such as communication logs and screen activity logs. Anomalies in such multivariate time series (MTS) have been shown to be predictive of relapse for patients with schizophrenia [3,4] and depressive symptoms for women at risk of perinatal depression [5]. The method was applied to a passively collected smartphone behavioral dataset to detect an escalation of symptoms or signs of a potential relapse. The error components are used to build Hotelling’s
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