A comparison between ceiling-mounted FMCW, IR-UWB and Wi-Fi radar for in-bedroom human activity monitoring and sleep interruption detection
2026-08-20 • Machine Learning
Machine Learning
AI summaryⓘ
The authors compared three types of radio systems—FMCW radar, IR-UWB, and Wi-Fi sensing—mounted on ceilings to monitor human activities and sleep in healthcare settings. They tested all three under the same conditions with 20 people in different rooms, using the same AI method to analyze results. IR-UWB worked best for recognizing different activities across people, while FMCW was better at handling new room setups. All systems did well in sleep monitoring. Their study shows each system has its own strengths depending on the environment, helping guide future healthcare tech choices.
FMCW radarImpulse Radio Ultra-Wideband (IR-UWB)Wi-Fi sensingHuman Activity Recognition (HAR)Sleep MonitoringConvolutional Neural Network (CNN)Cross-subject evaluationRange resolutionDoppler resolutionAntenna diversity
Authors
Anton Lambrecht, Reda El Hail, Xianjun Jiao, Pieter Crombez, Dominique Schreurs, Peter Karsmakers, Adnan Shahid, Eli De Poorter
Abstract
Despite their growing importance for contact-free radio frequency (RF) based healthcare monitoring, different radio technologies such as frequency-modulated continuous wave (FMCW) radar, impulse radio ultra-wideband (IR-UWB), and Wi-Fi sensing are rarely compared under identical deployment conditions, as existing studies typically differ in hardware, datasets, and evaluation methodologies. In addition, the performance of ceiling-mounted radars, despite their practical deployment and cost advantages in healthcare environments, remain underexplored. Therefore, this paper presents a controlled comparison and analysis of ceiling-mounted FMCW, IR-UWB, and Wi-Fi sensing using synchronized recordings from 20 participants across six room layouts. All technologies are evaluated with the same convolutional neural network (CNN) on both a fine-grained 10-class human activity recognition (HAR) task and a coarse 4-class sleep monitoring task. IR-UWB achieves the highest cross-subject activity recognition performance (89.0% macro F1), while FMCW generalizes best to unseen room layouts (83.8% macro F1). For sleep monitoring, all technologies exceed 92% macro F1 in unseen environments. The results reveal a fundamental trade-off between recognition performance and environmental robustness, which can be explained through differences in range resolution, antenna diversity, Doppler resolution, and spatial information retention. These findings provide practical guidelines for the design of healthcare-oriented RF sensing systems.