  Radio technology enabled contact-free human posture and vital sign estimation
is promising for health monitoring. Radio systems at millimeter-wave (mmWave)
frequencies advantageously bring large bandwidth, multi-antenna array and beam
steering capability. \textit{However}, the human point cloud obtained by mmWave
radar and utilized for posture estimation is likely to be sparse and
incomplete. Additionally, human's random body movements deteriorate the
estimation of breathing and heart rates, therefore the information of the chest
location and a narrow radar beam toward the chest are demanded for more
accurate vital sign estimation.
  In this paper, we propose a pipeline aiming to enhance the vital sign
estimation performance of mmWave FMCW MIMO radar. The first step is to
recognize human body part and posture, where we exploit a trained Convolutional
Neural Networks (CNN) to efficiently process the imperfect human form point
cloud. The CNN framework outputs the key point of different body parts, and was
trained by using RGB image reference and Augmentative Ellipse Fitting Algorithm
(AEFA). The next step is to utilize the chest information of the prior
estimated human posture for vital sign estimation. While CNN is initially
trained based on the frame-by-frame point clouds of human for posture
estimation, the vital signs are extracted through beamforming toward the human
chest.
  The numerical results show that this spatial filtering improves the
estimation of the vital signs in regard to lowering the level of side harmonics
and detecting the harmonics of vital signs efficiently, i.e., peak-to-average
power ratio in the harmonics of vital signal is improved up to 0.02 and 0.07dB
for the studied cases.
