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Multi-modal interactive fusion method for detecting teenagers' psychological stress
Huijun Zhang1, Lei Cao1, Ling Feng1
1Department of Computer Science and Technology, Tsinghua University, Beijing, China.
Journal of Biomedical Informatics
|April 28, 2020
Summary
Early detection of psychological stress in teens is crucial. A new mobile app method using multiple data types, like text and sleep, effectively detects stress better than single-source methods.
Area of Science:
- Psychology
- Computer Science
- Mobile Health
Background:
- Teenagers face increasing psychological stress, leading to physical and mental health issues.
- Early stress detection is vital for effective management and alleviation.
- Smartphones offer a feasible platform for monitoring daily behaviors and managing stress.
Purpose of the Study:
- To propose and evaluate a Multi-modal Interactive Fusion Method (MIFM) for detecting psychological stress in teenagers.
- To leverage smartphone-collected data for stress monitoring and management.
Main Methods:
- Developed a mobile app, Happort, to collect text, image, sleep, and exercise data.
- Implemented the Multi-modal Interactive Fusion Method (MIFM) incorporating attention mechanisms.
- Characterized associations between different data modalities to calculate individual contributions.
Main Results:
- The proposed Multi-modal Interactive Fusion Method (MIFM) demonstrated superior performance in psychological stress detection.
- Fusing multi-modal data sources consistently outperformed single-modality approaches.
- Attention mechanisms effectively determined the contribution of each data type.
Conclusions:
- Multi-modal data fusion is a highly effective strategy for psychological stress detection in adolescents.
- The Happort app and MIFM provide a promising tool for early stress identification and intervention.
- Integrating diverse data streams enhances the accuracy and reliability of stress monitoring systems.
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