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Automatic Depression Prediction Using Internet Traffic Characteristics on Smartphones.

Chaoqun Yue¹, Shweta Ware¹, Reynaldo Morillo¹

¹Department of Computer Science & Engineering, University of Connecticut, 371 Fairfield Way, Unit 4155, Storrs, 06269, CT, USA.

Smart Health (Amsterdam, Netherlands)

|October 12, 2020

Summary

Smartphone internet traffic data can help screen for depression. This study introduces novel features from internet usage patterns to effectively predict depression, achieving a high F1 score.

Keywords:

Data Analytics Depression Prediction Internet Traffic Characteristics Machine Learning Smartphone Sensing

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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

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Published on: December 11, 2015

Area of Science:

Digital phenotyping
Computational psychiatry
Machine learning in healthcare

Background:

Depression is a significant mental health concern.
Previous research utilized smartphone sensing data for depression screening.
Limited exploration of smartphone internet traffic data for this purpose.

Purpose of the Study:

To investigate the utility of smartphone internet traffic metadata for depression screening.
To develop novel features from internet usage sessions.
To build and evaluate machine learning models for depression prediction using these features.

Main Methods:

Collected coarse-grained internet traffic metadata from smartphones.
Developed techniques to identify internet usage sessions.
Extracted features based on internet usage patterns.
Trained machine learning models for depression prediction.

Main Results:

Internet usage features effectively differentiate between depressed and non-depressed individuals.
Features align with established psychological findings on behavioral characteristics.
Machine learning models achieved high accuracy in depression prediction (F1 score up to 0.80).

Conclusions:

Smartphone internet traffic metadata is a viable and low-energy source for depression screening.
Novel features derived from internet usage can significantly aid in depression prediction.
This approach offers a promising avenue for objective and scalable mental health monitoring.