StressMon: Large scale detection of stress and depression in campus environment using passive coarse-grained location data

• Main Author: ZAKARIA, Camellia
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Location information; monitoring system; Stress detection; Depression detection; mobility data; Mental and Social Health; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/etd_coll/258; https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1255&context=etd_coll
• Institution: Singapore Management University

The rising mental health illnesses of severe stress and depression is of increasing concern worldwide. Often associated by similarities in symptoms, severe stress can take a toll on a person’s productivity and result in depression if the stress is left unmanaged. Unfortunately, depression can occur without any feelings of stress. With depression growing as a leading cause of disability in economic productivity, there has been a sharp rise in mental health initiatives to improve stress and depression management. To offer such services conveniently and discreetly, recent efforts have focused on using mobile technologies. However, these initiatives usually require users to install dedicated apps or use a variety of sensors, making such solutions hard to scale. Moreover, they emphasise sensing individual factors and overlook ‘physical social interaction’ that plays a significant role in influencing stress and depression. This thesis presents StressMon, a monitoring system that can easily scale across entire campuses by passively sensing location information directly from the WiFi infrastructure.This dissertation explores how, by using only single-attribute location information, mobility features can be comprehensively extracted to represent individual behaviours to detect stress and depression accurately; it is important to note that this is without requiring explicit user actions or software installation on client devices. To overcome the low-dimensional data, StressMon additionally infers physical group interaction patterns from a group detector system. First, I investigate how mobility features can be exploited to better capture the dynamism of natural human behaviours indicative of stress and depression. Then, I present the framework to detect stress and depression accurately, albeit separately. In a supplementary effort, I demonstrate how optimising StressMon with group-based mobility features greatly enhances the performance of stress detection, and conversely, individual-based features improve depression detection. To extensively validate the system, I conducted three different semester-long longitudinal studies with different groups of undergraduate students at separate times, totalling up to 108 participants. Finally, this dissertation documents the differences learned in understanding stress and depression from a qualitative perspective.