  This work presents the use of frequency modulated continuous wave (FMCW)
radar technology combined with a machine learning model to differentiate
between normal and abnormal breath rates. The proposed system non-contactly
collects data using FMCW radar, which depends on breath rates. Various support
vector machine kernels are used to classify the observed data into normal and
abnormal states. Prolonged experiments show good accuracy in breath rate
classification, confirming the model's efficacy. The best accuracy is 95
percent with the smallest number of support vectors in the case of the
quadratic polynomial kernel.
