Sungkyu Park
Biographic Data
| ID | 6038199 |
|---|---|
| NAME | Sungkyu Park |
| GIVEN NAMES | Sungkyu |
| FAMILY NAME | Park |
| SIGNATURE | PARK S |
| AFFILIATIONS | Korea Development Institute |
| ORCID | 0000-0002-2607-2120 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 1 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2010 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 1 |
Predicting sleep quality with digital biomarkers and artificial neural networks
This work introduces a novel approach for managing sleep health through continuous HRV monitoring and predictive modeling using wearable devices. The findings highlight the potential of the LF/HF ratio as a digital biomarker for sleep quality prediction, offering promise for personalized, data-driven healthcare interventions. The superior performance of deep learning methods underscores the value of temporal pattern recognition in sleep quality a…
Association between heart rate variability metrics from a smartwatch and self-reported depression and anxiety symptoms
Our findings indicate the potential of PPG-derived HRV metrics in monitoring mental health, thereby providing a foundation for further research. Notably, parasympathetically biased HRV metrics showed weaker correlations with depression and anxiety scores. Future studies should validate these findings in clinically diagnosed patients
Eminent domain power and afterwards
Eminent domain power and afterwards
Association between heart rate variability metrics from a smartwatch and self-reported depression and anxiety symptoms
Our findings indicate the potential of PPG-derived HRV metrics in monitoring mental health, thereby providing a foundation for further research. Notably, parasympathetically biased HRV metrics showed weaker correlations with depression and anxiety scores. Future studies should validate these findings in clinically diagnosed patients
Predicting sleep quality with digital biomarkers and artificial neural networks
This work introduces a novel approach for managing sleep health through continuous HRV monitoring and predictive modeling using wearable devices. The findings highlight the potential of the LF/HF ratio as a digital biomarker for sleep quality prediction, offering promise for personalized, data-driven healthcare interventions. The superior performance of deep learning methods underscores the value of temporal pattern recognition in sleep quality a…
Computer Science (2 works) · Heart rate (2 works) · Heart rate variability (2 works) · Heart Rate Variability and Autonomic Control (2 works) · Internal Medicine (2 works) · Medicine (2 works) · Non-Invasive Vital Sign Monitoring (2 works) · Psychiatry (2 works) · Abuse of power (1 work) · Accountability (1 work)