Motivation
Mental-health technology requires both technical care and privacy sensitivity. The goal here is to explore signal extraction and interpretation without defaulting to cloud-heavy workflows.
Research
Real-Time Depression Detection via Micro-Expressions
An ongoing research direction exploring privacy-aware, real-time depression-related signal analysis through micro-expression cues and on-device processing.
2025 - ongoing
Project overview
This project studies whether short facial dynamics and micro-expression features can support more privacy-aware mental-health signal interpretation in a mobile context.
Method and architecture
- Short, user-consented camera snippet analysis
- Feature exploration around blink rate, asymmetry, and facial action cues
- Design direction centered on local or edge-friendly processing
My contribution
- Defined a research direction around privacy-first sensing
- Framed the problem as both a technical and ethical design challenge
- Connected mobile-system thinking with affective-computing research
Impact
- Represents a meaningful intersection of software engineering and health-AI research
- Helps shape a future portfolio direction in privacy-sensitive digital health systems