Depression has been a serious mental illness for a long time, which significantly influence people’ life quality. Meanwhile, as smartphone becomes an integral part of peoples life, it creates the opportunity to analyze users feelings through phone usage and sensor data. However, previous studies mainly adopt machine-learning methods for depression detection, ignoring the sequential patterns hidden in it. In this study, we aim to monitor the symptoms of depression through sequential mobile data collected from phone and its sensors. First, we establish a deep-learning model called Dep-caser to fully utilize the sequential information in 
mobile data. Next, we introduce a discretization method based on Information Value to deal with data sparsity and outliers. In total, we recruited 257 people to join the study and extracted five-day longitudinal data from their smartphones and electronic bands. We conduct two experiments to examine the effectiveness of Dep-caser and discretization method respectively. The results demonstrate that Dep-caser outperforms most of the machine learning methods and the discretization further improves the performance of the deep-learning model to achieve an overall accuracy of 0.83. Our study shows the promising future to adopt deep-learning models with sequential phone usage and sensing data to detect depression.

https://ieeexplore.ieee.org/document/9928499/