Graduation Semester and Year
2020
Language
English
Document Type
Thesis
Degree Name
Master of Science in Computer Science
Department
Computer Science and Engineering
First Advisor
Fillia Makedon
Second Advisor
Maria Kyrarini
Abstract
With the ubiquitous presence of mobile phones, there is too much data that can be collected for different purposes which can be correlated to monitor multiple more user information. If used responsibly, this data can be used to assess and monitor various mental health conditions. For example, fitness sensors in wearable devices collect step counts, sleep-related data and heart rate; GPS sensors in smartphones continually collect device location, and Android on-device services collect smartphone usage habits of the user. All the data from the previous example relate to exhibited depression symptoms in users. This data, when used in conjugation, has the potential to assess and monitor such symptoms in users. In this study, we collect the mentioned user data anonymously to analyze and inspect our hypotheses.
Keywords
Depression, Mobile application, Azul, Android, PHQ
Disciplines
Computer Sciences | Physical Sciences and Mathematics
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Srivastava, Manu, "Azul - Using multimodal sensors from mobile and wearable devices for assessment and monitoring of depression symptoms" (2020). Computer Science and Engineering Theses. 472.
https://mavmatrix.uta.edu/cse_theses/472
Comments
Degree granted by The University of Texas at Arlington