Monitoring Behavioral Changes Using Spatiotemporal Graphs: A Case Study on the StudentLife Dataset
Year: 2024
Type: conference-paper
Abstract: This paper introduces a novel method for monitoring behavioral changes in university students by constructing spatiotemporal graphs from smartphone sensor data. Utilizing the Student Life dataset, which collects multi-modal data from smartphone sensors over a 10-week period, we capture detailed aspects of student behavior, including location, physical activity, and self-reported stress. By representing this data as spatiotemporal graphs, we model behavioral evolution across both temporal and spatial dimensions, employing a spatiotemporal Graph Neural Network (STGNN) to detect patterns associated with stress, sleep quality, and academic performance. This method enables a dynamic, high-resolution analysis of student well-being, offering a more comprehensive understanding of behavior over time.
Authors: Harit, Anoushka, Sun, Zhongtian, Yu, Jongmin, Moubayed, Noura Al
Institutions: University of Cambridge, Cancer Research UK
Language: English
Subfield: Transportation
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W6992853616
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