  Estimating human vital signs in a contactless non-invasive method using radar
provides a convenient method in the medical field to conduct several health
checkups easily and quickly. In addition to monitoring while sitting and
sleeping, the standing position has aroused interest for both the industrial
and medical fields. However, it is more challenging due to the micro motions
induced by the body for balancing that may cause false respiratory rate
estimation. In this work, we focus on the measurement of the respiratory rate
of a standing person accurately with the capability of heavy breath detection
and estimation. Multiple estimation approaches are presented and compared,
including spectral estimation, deep-learning-based approaches, and adaptive
peak selection with Kalman filtering. The latest technique is showing the best
performance with an absolute error rate of 1.5 bpm, when compared to a Vernier
Go Direct\textsuperscript{\textregistered} respiration belt.
