Low-cost Embedded Breathing Rate Determination Using 802.15.4z IR-UWB Hardware for Remote Healthcare
Journal:
arXiv
Published Date:
Apr 3, 2025
Abstract
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 exploited to estimate human respiration rates. We propose a convolutional
neural network (CNN) to predict breathing rates from ultra-wideband (UWB)
channel impulse response (CIR) data, and compare its performance with other
rule-based algorithms. The study uses a diverse dataset of 16 individuals,
incorporating various real-life environments to evaluate system robustness.
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,
the system can last up to 268 days without recharging using 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.
