AI & ML
A Synchronized Tri-Modality Wireless System for Multi-Point Non-Contact Cardiac Monitoring
arXiv eess.SY0 views
arXiv:2611.07358v1 Announce Type: cross Abstract: Cardiac physiological monitoring is fundamental to evaluating cardiovascular health and diagnosing related diseases. Non-contact radar sensing can capture chest-wall vibrations with spatial specificity, enabling localized physiological monitoring and cardiac assessment. Progress, however, has been limited by the absence of a reliable, research-grade hardware platform for rigorous validation of radar-based methods. Prior systems typically pair radar, namely Doppler cardiography (DCG), with only electrocardiography (ECG) or other coarse references, providing no direct mechanical ground truth and thus constraining fine-grained evaluation of radar outputs. We present a tri-modality wireless system that integrates ECG, seismocardiography (SCG), and DCG under a unified acquisition and control framework for time-aligned recording and analysis. The tri-modality system combines miniaturized wearable ECG/SCG modules with a high-resolution mmWave radar module, supporting spatially resolved sensing and subsequent feature extraction. To our knowledge, this is the first integrated ECG--SCG--DCG hardware system for comprehensive cardiac monitoring; it enables multi-point thoracic acquisition, achieves precise cross-modal synchronization, and maintains specifications suitable for wearable operation. Experiments on a linear-motion platform show close agreement between DCG- and SCG-derived displacements, confirming accurate displacement measurement. Human-subject studies further demonstrate cross-modal consistency and robust extraction of feature points, establishing ECG as the electrical anchor and SCG as the mechanical ground truth for DCG. Overall, the system provides a rigorous, spatially resolved validation platform that advances non-contact cardiac sensing and supports next-generation radar-based monitoring.
Source:https://arxiv.org/abs/2611.07358PreviousAn Ordering Optimization for Fruit-Vegetable in High-Density Environment based on Genetic AlgorithmNextDynamic Watermarking and Action-Conditioned Latent Dynamics for Secure Vision-Based Controllers
Related posts
Thinking Machines’ new model takes the lead in western open-weight AI
0ZDNet
Amazon Deals of the Day: Clean Up Before the Holidays With Over $100 Off the Dyson Ball Animal Vacuum
0CNET News
Q&A: How is American manufacturing evolving with AI and robotics?
0Phys.org Tech
AI learns from the products of thinking, not thought itself. That's a problem for machines—and for us
0Phys.org Tech
Quoting Nilay Patel
0Simon Willison
Run OpenClaw (Formerly Moltbot and Clawdbot) in a Sprite
0Fly.io Blog