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Related Experiment Video

Updated: May 27, 2026

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Measuring multi-site pulse transit time with an AI-enabled mmWave radar.

Jiangyifei Zhu1, Kuang Yuan1, Akarsh Prabhakara2

  • 1Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.

Nature Communications
|May 25, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an AI-powered radar system for contactless Pulse Transit Time (PTT) measurement, a key indicator of cardiovascular health. The system accurately estimates blood pressure and arterial stiffness non-invasively across multiple body sites.

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Area of Science:

  • Biomedical Engineering
  • Cardiovascular Physiology
  • Artificial Intelligence in Healthcare

Background:

  • Pulse Transit Time (PTT) is a validated physiological marker for assessing arterial stiffness and cardiovascular function.
  • An inverse relationship exists between PTT and diastolic blood pressure (DBP).
  • Current methods for PTT measurement often require physical contact, limiting continuous monitoring and multi-site assessment.

Purpose of the Study:

  • To develop and evaluate an AI-enabled, contactless mmWave radar system for simultaneous multi-site PTT measurement and DBP estimation.
  • To assess the system's accuracy against established contact-based sensors.
  • To determine if the system meets regulatory guidelines for non-invasive blood pressure monitoring.

Main Methods:

  • Utilized a single mmWave radar system incorporating beamforming and deep learning algorithms.
  • Performed contactless PTT measurements across three physiological pathways: heart-to-radial, heart-to-carotid, and mastoid area-to-radial arteries.
  • Validated DBP estimation against reference sensors, comparing results against FDA AAMI guidelines.

Main Results:

  • Achieved correlation coefficients of 0.75-0.86 for PTT measurements compared to contact-based sensors.
  • Demonstrated high correlation coefficients (0.90-0.91) for DBP estimation.
  • Met FDA AAMI guidelines for non-invasive blood pressure monitors with a mean error of -0.62-0.06 mmHg and standard deviation of 4.54-5.20 mmHg.

Conclusions:

  • The proposed AI-enabled mmWave radar system offers a promising non-invasive method for assessing cardiovascular health.
  • Simultaneous multi-site PTT measurement and DBP estimation can be achieved contactlessly.
  • The system's performance meets clinical standards, suggesting potential for widespread application in health monitoring.