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Fusing Wearable Biosensors with Artificial Intelligence for Mental Health Monitoring: A Systematic Review
Ali Kargarandehkordi1, Shizhe Li2, Kaiying Lin1,3
1Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI 96822, USA.
Biosensors
|April 25, 2025
Summary
Wearable biosensors and AI can monitor mental health remotely. Challenges like data issues and battery life exist, but opportunities for AI-driven mental health biosensing are emerging.
Area of Science:
- Digital health
- Biomedical engineering
- Artificial intelligence
Background:
- Wearable devices offer remote, objective mental health monitoring.
- Biosensor data provides quantitative benchmarks for mental health conditions.
Purpose of the Study:
- To review artificial intelligence (AI) models for mental health prediction using wearable biosensor data.
- To identify trends and challenges in AI-driven mental health biosensing.
Main Methods:
- Systematic review following PRISMA guidelines.
- Included 48 studies using various wearable and smartphone biosensors (e.g., heart rate, HRV, EDA/GSR, accelerometry, audio).
- Analyzed AI models predicting mental health conditions and symptoms.
Main Results:
- Diverse biosensors and AI approaches are employed.
- Common challenges include lack of ecological validity, data heterogeneity, small sample sizes, and battery issues.
- AI shows potential in analyzing complex biosignals for mental health.
Conclusions:
- AI-powered wearable biosensing holds promise for mental health.
- Addressing methodological and technical challenges is crucial for advancing the field.
- Future research should focus on improving ecological validity and data standardization.
