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The paper presents a low-cost, embedded system for determining breathing rates using an IEEE 802.15.4z IR-UWB radar and a custom CNN architecture. The CNN predicts breathing rates directly from UWB channel impulse response (CIR) data, outperforming rule-based and model-based methods with a mean absolute error of 1.73 BPM, further reduced to 0.84 BPM with calibration data. The study demonstrates the feasibility of deploying the quantized CNN on a low-cost nRF52840 SoC with minimal performance degradation, achieving long battery life for continuous remote healthcare monitoring.
A CNN-based breathing rate monitor, powered by low-cost UWB radar and deployable on a tiny embedded device, rivals traditional methods and can run for almost a year on a battery pack.
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/ouput tensors, reduces memory requirements by 67% and inference time by 64% 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 192 ms. Once deployed, an analytical energy model estimates that the system, while continuously monitoring the room, can operate for up to 268 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 days, making the solution highly efficient for real-world, low-cost deployments.
