  The behaviors of patients with depression are usually difficult to predict
because the patients demonstrate the symptoms of a depressive episode without a
warning at unexpected times. The goal of this research is to build algorithms
that detect signals of such unusual moments so that doctors can be proactive in
approaching already diagnosed patients before they fall in depression. Each
patient is equipped with a smartphone with the capability to track its sensors.
We first find the home location of a patient, which is then augmented with
other sensor data to identify sleep patterns and select communication patterns.
The algorithms require two to three weeks of training data to build standard
patterns, which are considered normal behaviors; and then, the methods identify
any anomalies in day-to-day data readings of sensors. Four smartphone sensors,
including the accelerometer, the gyroscope, the location probe and the
communication log probe are used for anomaly detection in sleeping and
communication patterns.
