Depression Diagnosis and Forecast based on Mobile Phone Sensor Data

by   Xiangheng He, et al.

Previous studies have shown the correlation between sensor data collected from mobile phones and human depression states. Compared to the traditional self-assessment questionnaires, the passive data collected from mobile phones is easier to access and less time-consuming. In particular, passive mobile phone data can be collected on a flexible time interval, thus detecting moment-by-moment psychological changes and helping achieve earlier interventions. Moreover, while previous studies mainly focused on depression diagnosis using mobile phone data, depression forecasting has not received sufficient attention. In this work, we extract four types of passive features from mobile phone data, including phone call, phone usage, user activity, and GPS features. We implement a long short-term memory (LSTM) network in a subject-independent 10-fold cross-validation setup to model both a diagnostic and a forecasting tasks. Experimental results show that the forecasting task achieves comparable results with the diagnostic task, which indicates the possibility of forecasting depression from mobile phone sensor data. Our model achieves an accuracy of 77.0 accuracy of 53.7 RMSE score of 4.094 (PHQ-9, range from 0 to 27).


A Bayesian Approach to Income Inference in a Communication Network

The explosion of mobile phone communications in the last years occurs at...

Continual Prediction of Notification Attendance with Classical and Deep Network Approaches

We investigate to what extent mobile use patterns can predict -- at the ...

DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection

The increasing use of electronic forms of communication presents new opp...

Inferring Mood-While-Eating with Smartphone Sensing and Community-Based Model Personalization

The interplay between mood and eating has been the subject of extensive ...

Learning Shallow Detection Cascades for Wearable Sensor-Based Mobile Health Applications

The field of mobile health aims to leverage recent advances in wearable ...

Recognizing Detailed Human Context In-the-Wild from Smartphones and Smartwatches

The ability to automatically recognize a person's behavioral context can...

Please sign up or login with your details

Forgot password? Click here to reset