01596nas a2200193   4500000000100000000000100001008004100002260001200043100001800055700001500073700001400088700001100102700001900113700001700132700001700149245009100166490000800257520113700265       2024                            d  c12/20241 aMinqiang Yang1 aEdith Ngai1 aXiping Hu1 aBin Hu1 aJiangchuan Liu1 aErol Gelenbe1 aVictor Leung00aDigital Phenotyping and Feature Extraction on Smartphone Data for Depression Detection0 v1123 a<p>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.</p>