Harit, Anoushka, Sun, Zhongtian, Yu, Jongmin, Moubayed, Noura Al (2024) Monitoring Behavioral Changes Using Spatiotemporal Graphs: A Case Study on the StudentLife Dataset. In: NeurIPS 2024 Workshop on Behavioral Machine Learning. . (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:108670)
The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication) | |
Official URL: https://openreview.net/forum?id=WyAKFNSYl2 |
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.
Item Type: | Conference or workshop item (Paper) |
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Subjects: | Q Science > Q Science (General) > Q335 Artificial intelligence |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing |
Depositing User: | Zhongtian Sun |
Date Deposited: | 06 Feb 2025 16:01 UTC |
Last Modified: | 11 Feb 2025 09:26 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/108670 (The current URI for this page, for reference purposes) |
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