Multicenter Evaluation of Machine-Learning Continuous Pulse Rate Algorithm on Wrist-Worn Device
The analytical validation conducted in this study demonstrated clinical-grade accuracy and generalizability of ML-based continuous PR estimations across a full range of physical motions, health conditions, and demographic variables known to confound PPG signals, paving the way for device usage by populations most likely to benefit from continuous PR monitoring.
Authors
- Jessie Lever Taylor (ORCID: https://orcid.org/0000-0001-8595-8733)
- Weixuan Chen
- Marisa Cruz (ORCID: https://orcid.org/0009-0009-4703-1908)
- Giulia Regalia (ORCID: https://orcid.org/0000-0002-6577-9057)
- Rafael Cordero (ORCID: https://orcid.org/0000-0002-1213-0051)
- Domenico R. Pangallo
- Rosalind W. Picard (ORCID: https://orcid.org/0000-0002-5661-0022)
Institutions
- Medieval Academy of America (US)
- Massachusetts Institute of Technology (US)
Publication Details
- Journal
- Digital Biomarkers
- Published
- 2024-12-12
- DOI
- https://doi.org/10.1159/000542615
- Citations
- 1
- Primary Topic
- Non-Invasive Vital Sign Monitoring
- Type
- article
- Field-Weighted Citation Impact
- 0.18