Comparing Ceiling-Mounted FMCW, IR-UWB, and Wi-Fi Radar for Contactless Health Monitoring

Forum topic · 小凯 · 2026-08-22

Summary

This arXiv paper (2608.20322) by Anton Lambrecht, Reda El Hail, Xianjun Jiao, and Pieter Crombez presents a controlled comparison of ceiling-mounted FMCW radar, IR-UWB, and Wi-Fi sensing for RF-based contactless health monitoring. The authors recorded synchronized data from 20 participants across six room layouts and evaluated all three technologies using the same CNN on two tasks: fine-grained 10-class human activity recognition and coarse-grained 4-class sleep monitoring. IR-UWB achieved the best cross-subject activity recognition performance (89.0% macro F1), while FMCW radar generalized best to unseen room layouts (83.8% macro F1). For sleep monitoring, all technologies exceeded 92% macro F1 in unseen environments. The results reveal a fundamental trade-off between recognition performance and environmental robustness, explained by differences in range resolution, antenna diversity, Doppler resolution, and spatial information retention. Ceiling-mounted deployment, attractive for practical installations and cost, remained underexplored prior to this work.

A Comparison Between Ceiling-Mounted FMCW, IR-UWB, and Wi-Fi Radar for Contactless Health Monitoring

Field: Machine Learning
Authors: Anton Lambrecht, Reda El Hail, Xianjun Jiao, Pieter Crombez
Published: 2026-08-22
arXiv: 2608.20322

Overview

Although RF-based contactless health monitoring is increasingly important, the different radio technologies involved—FMCW radar, IR-UWB, and Wi-Fi sensing—are rarely compared under identical deployment conditions. Moreover, the performance of ceiling-mounted radar, despite its advantages for real-world deployment and cost, remains underexplored.

Methodology

Controlled comparison of FMCW, IR-UWB, and Wi-Fi sensing using synchronized recordings from 20 participants across six room layouts.
All technologies evaluated with the same CNN on:
Fine-grained 10-class human activity recognition
Coarse-grained 4-class sleep monitoring

Key Results

IR-UWB achieved the best cross-subject activity recognition: 89.0% macro F1.
FMCW generalized best to unseen room layouts: 83.8% macro F1.
For sleep monitoring, all technologies exceeded 92% macro F1 in unseen environments.

Conclusion

The results reveal a fundamental trade-off between recognition performance and environmental robustness, which can be explained by differences in range resolution, antenna diversity, Doppler resolution, and spatial information retention.

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*Automatically collected on 2026-08-22*