Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis
Abstract
One of the most interesting applications of mobile sensing is monitoring of individual behavior, especially in the area of mental health care. Most existing systems require an interaction with the device, for example they may require the user to input his/her mood state at regular intervals. In this paper we seek to answer whether mobile phones can be used to unobtrusively monitor individuals affected by depressive mood disorders by analyzing only their mobility patterns from GPS traces. In order to get ground-truth measurements, we have developed a smartphone application that periodically collects the locations of the users and the answers to daily questionnaires that quantify their depressive mood. We demonstrate that there exists a significant correlation between mobility trace characteristics and the depressive moods. Finally, we present the design of models that are able to successfully predict changes in the depressive mood of individuals by analyzing their movements.
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Distance and radius based on locations were shown to be important in previous studies.
“For mobility features, we computed the distance and radius based on locations to which our participants travelled as these features were shown to be important in previous studies [8,48].”
The relationship between reduced activity levels and mobility patterns and high stress and low mental health has been studied.
“The relationship between reduced activity levels and mobility patterns and high stress and low mental health has been studied [48,65].”
Significant correlation exists between mobility trace characteristics and the depressive moods of individuals.
“GPS traces also produce similar conclusions [117]—significant correlation exists between mobility trace characteristics and the depressive moods of individuals.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [117]
Canzian et al showed a tendency of correlation between a range of GPS metrics similar to Saeb et al and self-reported depression scores.
“Canzian et al [18] were able to show a tendency of correlation between a range of GPS metrics similar to the ones presented by Saeb et al [17] and a self-reported depression scores.”
From Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild (2016) · cites this paper as [18]
Duration and time of the day of stays at different physical locations are related to a person's mental state and can be approximated by WiFi and global positioning systems (GPS) information.
“duration, and time of the day of stays at different physical locations were shown to be related to a person’s mental state and can be approximated by WiFi and global positioning systems (GPS) information [17,18].”
From Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild (2016) · cites this paper as [18]
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Classifying Depression in Imbalanced Datasets Using an Autoencoder- Based Anomaly Detection Approach
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- Authors
- Canzian, L., & Musolesi, M.
- Affiliations
- University of Birmingham (GB)
- University College London (GB)
- ORCID
- Musolesi, Mirco
- Venue
- Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing
- Publisher
- ACM
- Published
- 2015-09-07
- Pages
- 1293-1304
- OpenAlex
- W2066806488
- Cited by
- 477
- References
- 36
- Citations by year
- 2026: 24 · 2025: 23 · 2024: 38 · 2023: 39 · 2022: 64 · 2021: 58 · 2020: 56 · 2019: 57 · 2018: 52 · 2017: 43 · 2016: 21 · 2015: 2
- FWCI
- 65.10
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) (publishedVersion)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion)
- Funders
- EPSRC (EP/L006340/1)
- European Commission (318132)
- Topics
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Keywords
- depression, mobile sensing, GPS tracks, movement patterns, non-intrusive monitoring, mental health monitoring, mobile application, location data
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