ORIGINAL RESEARCH article
Front. Psychiatry
Sec. Digital Mental Health
Volume 16 - 2025 | doi: 10.3389/fpsyt.2025.1468334
Smartphone Sensor-Based Depression Detection in Campus Environments: A Proof-of-Concept Study with Small-Sample Behavioral Analysis
Provisionally accepted
- 1Lanzhou University, Lanzhou, China
- 2Community College, King Saud University, Riyadh, Saudi Arabia
- 3Cyberspace Administration of Lanzhou University, Lanzhou, China
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Depression is a rising global health issue, particularly among adolescents, with university students facing distinct mental health challenges. This proof-of-concept study explores smartphone sensor-based depression detection in Chinese university campus settings using a small sample of 12 participants. We utilized data from accelerometers, gyroscopes, and light sensors to establish associations between smartphone-derived behavioral patterns and PHQ-9 scores, a standard depression measure. A customized data processing scheme tailored to campus life enabled the extraction of 18 feature sequences reflecting depressive symptoms. Feature selection was conducted using Pearson correlation, and model validation was performed using leave-one-out cross-validation with common classification algorithms. The results yielded accuracy rates between 73.11% and 88.24%. Findings showed negative correlations between PHQ-9 scores and dietary regularity, bedtime, and physical activity levels. This pioneering study highlights smartphone sensors' potential for early depression detection in Chinese higher education, supporting non-invasive mental health interventions.
Keywords: Depression, feature, daily life behaviors, Smartphone sensors, machine learning
Received: 24 Jul 2024; Accepted: 03 Jul 2025.
Copyright: © 2025 Bai, Liu, Tolba and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yang Zhang, Cyberspace Administration of Lanzhou University, Lanzhou, China
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