Digital Phenotyping and Feature Extraction on Smartphone Data for Depression Detection

Minqiang Yang, Edith C.H. Ngai, Xiping Hu*, Bin Hu*, Jiangchuan Liu, Erol Gelenbe, Victor C.M. Leung

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Smartphones are widely used as portable data collectors for wearable and healthcare sensors that can passively collect data streams related to the environment, health status, and behaviors. Recent research shows that the collected data can be used to monitor not only the physical states but also the mental health of individuals. However, extracting the features of digital phenotypes that characterize major depressive disorder (MDD) is technically challenging and may raise significant privacy concerns. Addressing such challenges has become the focus of many researchers. This article provides a comprehensive analysis of several key issues related to ubiquitous sensing to aid in detecting MDD. Specifically, this article analyzes existing methodologies and feature extraction algorithms used to detect possible MDD through digital phenotyping from smartphone data. In particular, five types of features are summarized and explained, namely, location, movement, rhythm, sleep, and social and device usage. Finally, related limitations and challenges are discussed to provide paths for further research and engineering.

Original languageEnglish
Pages (from-to)1773-1798
Number of pages26
JournalProceedings of the IEEE
Volume112
Issue number12
DOIs
Publication statusPublished - 2024
Externally publishedYes

Keywords

  • Depression detection
  • digital phenotyping
  • feature extraction
  • sensors
  • smartphone

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