Multi-Bin Breathing Pattern Estimation by Radar Fusion for Enhanced
Driver Monitoring
Abstract
Monitoring the status of the driver is a crucial aspect of health
monitoring inside vehicles as it helps to identify potential health or
safety risks that could affect a driver’s ability to operate a vehicle
safely. This includes monitoring for fatigue, distraction, or
impairment, among other things, which can potentially cause car crashes.
Although many solutions for health monitoring in private vehicles have
been proposed, the majority of them are inconvenient to use or have the
risk of leaking private information. Radars have the potential to
address the above drawbacks by their inherent privacy protection and
contactless operation in addition to their high accuracy, convenience,
affordable price, and resilience to environmental factors. Among many
possible radar configurations, millimeter FMCW radars can accurately
detect range and monitor displacements that are essential in breathing
pattern monitoring. Breathing pattern monitoring is one of the key
signatures of the driver’s health. An accurate estimation of the
breathing pattern enables the detection of breathing abnormalities,
including tachypnea, bradypnea, biot, cheyne–stokes, and apnea. The
breathing pattern can be estimated from both the chest and abdomen. For
this purpose, we employed two 60 GHz FMCW radars. The proposed algorithm
is capable of detecting the mentioned breathing abnormalities through
breathing rate (BR) estimation and breath-hold period detection. In
addition, the proposed method in this paper estimates BR based on the
multiple range bins. We conducted a study on the human radar geometry
problem inside a vehicle to determine the accurate number of range bins
for BR estimation. The experimental results demonstrate a maximum BR
error of 1.9 breaths per minute using the proposed multi-bin technique.
In addition, the dual radar fusion system can detect breath-hold periods
with minimal false detections.