用廉价雷达+神经网络实现高精度远程呼吸监测。
Low-cost Embedded Breathing Rate Determination Using 802.15.4z IR-UWB Hardware for Remote Healthcare
- 用CNN从雷达信号中提取呼吸率，比传统方法更准。
- 在未知环境下误差仅1.73次/分钟，校准后降至0.84次/分钟。
- 可部署在低功耗芯片上，电池续航超260天，适合居家使用。
呼吸系统疾病是全球死亡的重要原因。低成本、早期检测有助于应对这些疾病。本文采用符合IEEE 802.15.4z标准的商用射频雷达（冲激无线电超宽带，IR-UWB），通过卷积神经网络（CNN）从超宽带（UWB）信道冲激响应（CIR）数据中估计呼吸率，并与基于规则和模型的方法进行对比。研究使用涵盖多种真实场景的多样化数据集评估系统鲁棒性，该数据集将开源以支持后续研究。结果表明，该CNN在未见过的场景下平均绝对误差（MAE）为1.73次/分钟，显著优于规则方法（3.40次/分钟）。若引入其他个体的校准数据，误差进一步降低至0.84次/分钟。此外，本文评估了在低功耗嵌入式设备上的可行性：对权重和输入/输出张量进行8位量化后，内存占用减少67%，推理时间缩短62%，仅导致MAE增加3%。最终可在nRF52840系统级芯片（SoC）上部署，仅需46 KB内存，推理时间199毫秒。能量模型分析显示，连续监控房间时，20,000 mAh电池供电可运行268.2天；床边监测采样率降低后，续航可达313.8天，适用于低成本、实际部署场景。
原文摘要 · Abstract (English)
Respiratory diseases account for a significant portion of global mortality. Affordable and early detection is an effective way of addressing these ailments. To this end, a low-cost commercial off-the-shelf (COTS), IEEE 802.15.4z standard compliant impulse-radio ultra-wideband (IR-UWB) radar system is used to estimate human respiration rates. We propose a convolutional neural network (CNN) specifically adapted to predict breathing rates from ultra-wideband (UWB) channel impulse response (CIR) data, and compare its performance with both other rule-based algorithms and model-based solutions. The study uses a diverse dataset, incorporating various real-life environments to evaluate system robustness. To facilitate future research, this dataset will be released as open source. Results show that the CNN achieves a mean absolute error (MAE) of 1.73 breaths per minute (BPM) in unseen situations, significantly outperforming rule-based methods (3.40 BPM). By incorporating calibration data from other individuals in the unseen situations, the error is further reduced to 0.84 BPM. In addition, this work evaluates the feasibility of running the pipeline on a low-cost embedded device. Applying 8-bit quantization to both the weights and input/output tensors, reduces memory requirements by 67% and inference time by 62% with only a 3% increase in MAE. As a result, we show it is feasible to deploy the algorithm on an nRF52840 system-on-chip (SoC) requiring only 46 KB of memory and operating with an inference time of only 199 ms. Once deployed, an analytical energy model estimates that the system, while continuously monitoring the room, can operate for up to 268.2 days without recharging when powered by a 20 000 mAh battery pack. For breathing monitoring in bed, the sampling rate can be lowered, extending battery life to 313.8 days, making the solution highly efficient for real-world, low-cost deployments.
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