Presenter Information

Location

https://www.kennesaw.edu/ccse/events/computing-showcase/sp26-cday-program.php

Document Type

Event

Start Date

22-4-2026 4:00 PM

Description

Cardiovascular Disease (CVD) related most machine learning (ML) models trained on clinical data offer high accuracy but are not practical for continuous monitoring. Smartwatch-based wearables provide continuous real-time physiological data but lack clinical validation for robust risk prediction outside the clinical setting. To bridge this gap, we proposed a novel teacher-student knowledge distillation framework that transfers knowledge of complex and large EHR datasets to a small Fitbit smartwatch dataset-based prediction model. The student model achieves promising accuracy, identifying all types of derived CVD risk profile groups. Our study introduces a non-invasive continuous health monitoring framework, demonstrating that passively collected daily metrics can be transformed to clinically powerful early warnings of an individual’s long-term cardiovascular health plan.

Share

COinS
 
Apr 22nd, 4:00 PM

GRP-093-174 Transforming Everyday Smartwatch Data into Clinical Early Warnings

https://www.kennesaw.edu/ccse/events/computing-showcase/sp26-cday-program.php

Cardiovascular Disease (CVD) related most machine learning (ML) models trained on clinical data offer high accuracy but are not practical for continuous monitoring. Smartwatch-based wearables provide continuous real-time physiological data but lack clinical validation for robust risk prediction outside the clinical setting. To bridge this gap, we proposed a novel teacher-student knowledge distillation framework that transfers knowledge of complex and large EHR datasets to a small Fitbit smartwatch dataset-based prediction model. The student model achieves promising accuracy, identifying all types of derived CVD risk profile groups. Our study introduces a non-invasive continuous health monitoring framework, demonstrating that passively collected daily metrics can be transformed to clinically powerful early warnings of an individual’s long-term cardiovascular health plan.