UWB Radar-based Heart Rate Monitoring: A Transfer Learning Approach

2025-07-14

AI Summary

Heterogeneous millimeter-wave radar systems—such as Frequency-Modulated Continuous-Wave (FMCW) and Impulse Radio Ultra-Wideband (IR-UWB)—lack standardization for contactless heart rate monitoring, necessitating extensive paired data collection for each radar modality. Method: We propose the first cross-radar-modality transfer learning framework, based on a 2D+1D ResNet architecture: a high-resolution FMCW radar dataset is used for pretraining, followed by lightweight fine-tuning using only single-antenna, single-range-gate, low-resolution IR-UWB data. Contribution/Results: This work achieves the first successful knowledge transfer from FMCW to IR-UWB for heart rate estimation. Experimental results show that the transferred IR-UWB model achieves a mean absolute error (MAE) of 4.1 bpm (25% improvement over baseline), mean absolute percentage error (MAPE) of 6.3%, and recall of 97.5%; the source FMCW model attains MAE = 0.85 bpm and MAPE = 1.42%. The framework significantly enhances model generalizability while reducing data dependency and calibration costs for new radar deployments.

Technology Category

• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Radar technology presents untapped potential for continuous, contactless, and passive heart rate monitoring via consumer electronics like mobile phones. However the variety of available radar systems and lack of standardization means that a large new paired dataset collection is required for each radar system. This study demonstrates transfer learning between frequency-modulated continuous wave (FMCW) and impulse-radio ultra-wideband (IR-UWB) radar systems, both increasingly integrated into consumer devices. FMCW radar utilizes a continuous chirp, while IR-UWB radar employs short pulses. Our mm-wave FMCW radar operated at 60 GHz with a 5.5 GHz bandwidth (2.7 cm resolution, 3 receiving antennas [Rx]), and our IR-UWB radar at 8 GHz with a 500 MHz bandwidth (30 cm resolution, 2 Rx). Using a novel 2D+1D ResNet architecture we achieved a mean absolute error (MAE) of 0.85 bpm and a mean absolute percentage error (MAPE) of 1.42% for heart rate monitoring with FMCW radar (N=119 participants, an average of 8 hours per participant). This model maintained performance (under 5 MAE/10% MAPE) across various body positions and heart rate ranges, with a 98.9% recall. We then fine-tuned a variant of this model, trained on single-antenna and single-range bin FMCW data, using a small (N=376, avg 6 minutes per participant) IR-UWB dataset. This transfer learning approach yielded a model with MAE 4.1 bpm and MAPE 6.3% (97.5% recall), a 25% MAE reduction over the IR-UWB baseline. This demonstration of transfer learning between radar systems for heart rate monitoring has the potential to accelerate its introduction into existing consumer devices.

Problem

• Transfer learning between FMCW and IR-UWB radar systems for heart rate monitoring
• Reducing need for large new datasets per radar system via transfer learning
• Achieving accurate contactless heart rate monitoring across different radar types

Innovation

• Transfer learning between FMCW and IR-UWB radar systems
• 2D+1D ResNet architecture for heart rate monitoring
• Fine-tuning model with small IR-UWB dataset