Real-time multi-modal physiological monitoring for aging society
As the Principal Investigator of this WISET-funded research program, I am leading the development of a comprehensive wireless healthcare monitoring system designed for the aging society. This system integrates multiple physiological sensors including PPG, Bio-Z, and temperature sensors, with real-time BLE data transmission to a custom Android application.
The project showcases end-to-end system design from FPCB hardware development to deep learning algorithms for on-device signal processing, achieving medical-grade accuracy in vital sign monitoring while maintaining ultra-low power consumption suitable for continuous wearable use.
Real-time heart rate and SpO₂ measurement
Body composition and hydration analysis
Continuous core body temperature tracking
Android app for real-time visualization
MAX30102
AD5933
TMP117
nRF52840
Live graphs and waveforms of physiological signals
Continuous logging with cloud synchronization
Customizable thresholds for vital sign alerts
On-device deep learning for arrhythmia detection
Selected through national competitive process for WISET Research Program. Led grant proposal, team management, and ₩7,000,000 budget administration.
Designed complete hardware-software ecosystem from FPCB layout to Android app, demonstrating full-stack engineering capabilities.
Implemented Temporal Convolutional Networks (TCN) for real-time PPG signal analysis achieving 95% accuracy in heart rate variability detection.