Integrating Remote Photoplethysmography and Machine Learning on Multimodal Dataset for Noninvasive Heart Rate Monitoring
Rinaldi Anwar Buyung, Alhadi Bustamam, Muhammad Remzy Syah Ramazhan, Giovanni Saggio · 2024
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Description
Rinaldi Anwar Buyung and co-authors combine camera based pulse sensing with machine learning to estimate heart rate without touch in a 2024 study. The camera method reads tiny color shifts in skin as blood pulses, but lighting and skin tone can distort it. The team added body data to steady the estimate.
They recorded one minute face videos plus age, sex, weight, and height for 60 people, and derived body mass index. Two learning models were compared on the combined data. A random forest model did best, with a mean error near three beats per minute and a mean percentage error of 4.2 percent. The gains point to uses in remote care and mass screening.
Catalog record
| CREATOR | Rinaldi Anwar Buyung, Alhadi Bustamam, Muhammad Remzy Syah Ramazhan, Giovanni Saggio |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 52 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |