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The principle

With every heartbeat, the volume of blood in the skin rises and falls. Blood absorbs light, so the skin’s color changes very slightly in time with the pulse. A camera can detect this change even though it’s invisible to the eye. Measuring it without contact is called remote photoplethysmography (rPPG).

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How the Elata SDK does it

rppg-web runs this pipeline in the browser, on the user’s device:
  1. Find the face. A face-landmark model locates skin regions such as the forehead and cheeks.
  2. Average the color. For each video frame, the SDK averages the red, green, and blue values in those regions.
  3. Extract the pulse signal. Established methods combine the color channels to cancel out lighting and motion: the green-channel method, CHROM (chrominance-based), and POS (plane-orthogonal-to-skin).
  4. Estimate the rate. The SDK estimates beats per minute two ways, from the signal’s frequency spectrum and from its autocorrelation, and checks that they agree.
  5. Report quality. Every estimate comes with a signal-quality value and a confidence value, so an app can tell a clean reading from a guess.
Raw video frames never leave the device.

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What affects accuracy

rPPG works well under good conditions and degrades under poor ones. Published reviews consistently identify the same challenges: Tips for users are on Devices. Builders can use the SDK’s quality and confidence values to prompt users or hold back a reading.

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What rPPG can and can’t claim

  • Can: estimate pulse rate at rest in reasonable conditions, and show how steady the signal is.
  • With caution: heart-rate variability from a camera needs very clean, longer recordings.
  • Can’t: diagnose heart rhythm problems or replace a medical device. Even contact PPG, used in smartwatches, needs ECG confirmation for diagnosis.

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Sources

Peer-reviewed literature, retrieved via PubMed:
  • Debnath U, Kim S. A comprehensive review of heart rate measurement using remote photoplethysmography and deep learning. Biomedical Engineering Online, 2025. doi:10.1186/s12938-025-01405-5
  • Chen W, et al. Deep learning and remote photoplethysmography powered advancements in contactless physiological measurement. Frontiers in Bioengineering and Biotechnology, 2024. doi:10.3389/fbioe.2024.1420100
  • De Wever M, et al. Photoplethysmography for heart rate and rhythm monitoring: current evidence and clinical applications. JACC: Clinical Electrophysiology, 2026. doi:10.1016/j.jacep.2026.06.034
SDK implementation: rppg-web (public SDK repository).