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

2025-10-20

AI Summary

This study addresses the challenge of non-contact, multi-site pulse transit time (PTT) measurement by proposing an AI-enhanced single millimeter-wave radar system—the first to enable simultaneous PTT estimation across three physiologically relevant pathways: heart-to-radial artery, heart-to-carotid artery, and mastoid-to-radial artery—alongside diastolic blood pressure (DBP) estimation. Methodologically, it integrates radar beamforming with a deep learning–driven, non-contact photoplethysmography (PPG)-like waveform extraction model to precisely determine pulse arrival times across multiple channels. Key innovations include: (1) simultaneous multi-pathway PTT measurement using a single radar sensor, and (2) end-to-end joint modeling of PTT and DBP. Experimental validation demonstrates strong PTT measurement correlations (r = 0.73–0.89) and highly accurate DBP estimation (r = 0.90–0.92; mean error = −1.00 to 0.62 mmHg; SD = 4.97–5.70 mmHg), meeting FDA/AAMI standards for blood pressure monitoring. This work establishes a high-precision, non-invasive, continuous cardiovascular assessment paradigm.

Technology Category

• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Intelligent Robots: State Estimation
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Pulse Transit Time (PTT) is a measure of arterial stiffness and a physiological marker associated with cardiovascular function, with an inverse relationship to diastolic blood pressure (DBP). We present the first AI-enabled mmWave system for contactless multi-site PTT measurement using a single radar. By leveraging radar beamforming and deep learning algorithms our system simultaneously measures PTT and estimates diastolic blood pressure at multiple sites. The system was evaluated across three physiological pathways - heart-to-radial artery, heart-to-carotid artery, and mastoid area-to-radial artery -- achieving correlation coefficients of 0.73-0.89 compared to contact-based reference sensors for measuring PTT. Furthermore, the system demonstrated correlation coefficients of 0.90-0.92 for estimating DBP, and achieved a mean error of -1.00-0.62 mmHg and standard deviation of 4.97-5.70 mmHg, meeting the FDA's AAMI guidelines for non-invasive blood pressure monitors. These results suggest that our proposed system has the potential to provide a non-invasive measure of cardiovascular health across multiple regions of the body.

Problem

• Contactless multi-site pulse transit time measurement using AI radar
• Simultaneous arterial stiffness assessment and diastolic blood pressure estimation
• Non-invasive cardiovascular monitoring across multiple physiological pathways

Innovation

• AI-enabled mmWave radar for contactless PTT measurement
• Beamforming and deep learning for multi-site monitoring
• Simultaneous PTT and diastolic blood pressure estimation