跨雷达系统迁移学习，让手机毫米波雷达更准测心率。
UWB Radar-based Heart Rate Monitoring: A Transfer Learning Approach
- 用2D+1D ResNet模型，在60GHz FMCW雷达上实现0.85bpm误差。
- 用小规模8GHz IR-UWB数据微调，心率误差降至4.1bpm。
- 无需重新采集大样本，适合快速部署到新设备中。
雷达技术在消费电子中具有持续、无接触、被动监测心率的潜力。然而，雷达系统多样且缺乏标准化，导致每个新系统都需大量配对数据集。本研究展示了在频率调制连续波（FMCW）与脉冲无线电超宽带（IR-UWB）雷达之间的迁移学习能力。FMCW雷达工作于60 GHz，带宽5.5 GHz（2.7 cm分辨率，3个接收天线），而IR-UWB雷达工作于8 GHz，带宽500 MHz（30 cm分辨率，2个接收天线）。采用新型2D+1D ResNet架构，在119名参与者（每人平均8小时）上，FMCW雷达实现平均绝对误差（MAE）0.85 bpm，平均绝对百分比误差（MAPE）1.42%。该模型在不同体位和心率范围内均保持性能（<5 MAE / <10% MAPE），召回率达98.9%。随后，将仅使用单天线、单距离单元的FMCW数据训练的模型，用少量IR-UWB数据（N=376，每人平均6分钟）进行微调，得到的模型在测试中达到MAE 4.1 bpm，MAPE 6.3%（召回率97.5%），较原始IR-UWB基线降低25%的MAE。该迁移学习方法为心率监测在现有消费设备中的快速部署提供了可能。
原文摘要 · Abstract (English)
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.
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