Machine learning-based fatigue classification using heart rate variability and cortisol: A multimodal approach to wearable health monitoring
This study demonstrated that a fatigue prediction model based on cortisol and HRV can maintain significant predictive power with a reduced number of variables. These findings suggest the potential for practical implementation in wearable devices, enabling accurate fatigue monitoring while minimizing sensor count and computational burden.
Authors
- Keehyuck Lee (ORCID: https://orcid.org/0000-0001-6906-4887)
- Na Hyeon Kim (ORCID: https://orcid.org/0009-0009-8233-2167)
- S. K. Choi (ORCID: https://orcid.org/0009-0000-3391-9222)
- Joung Eun Kim (ORCID: https://orcid.org/0009-0002-1893-1156)
- Jong Soo Han (ORCID: https://orcid.org/0000-0001-9248-9998)
- Jiyoon Lee (ORCID: https://orcid.org/0000-0002-9145-1098)
Institutions
- Inha University (KR)
- Seoul National University Bundang Hospital (KR)
Publication Details
- Journal
- Digital Health
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1177/20552076251395570
- Primary Topic
- Sleep and Work-Related Fatigue
- Type
- article
- Field-Weighted Citation Impact
- 0.00