Enhancing Vital Sign Estimation Performance of FMCW MIMO Radar by Prior Human Shape Recognition

Hadi Alidoustaghdam, Min Chen, Ben Willetts, Kai Mao, Andre Kokkeler, Yang Miao

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review


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. 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 0.07 dB for the studied cases.

Original languageEnglish
Title of host publication2023 IEEE International Conference on Communications Workshops
Subtitle of host publicationSustainable Communications for Renaissance, ICC Workshops 2023
Place of PublicationPiscataway, NJ
Number of pages5
ISBN (Electronic)979-8-3503-3307-7
ISBN (Print)979-8-3503-3308-4
Publication statusPublished - 23 Oct 2023
Event2023 IEEE International Conference on Communications Workshops, ICC Workshops 2023 - Rome, Italy
Duration: 28 May 20231 Jun 2023


Conference2023 IEEE International Conference on Communications Workshops, ICC Workshops 2023
Abbreviated titleIEEE ICC 2023


  • Augmentative ellipse fitting algorithm
  • Beamforming
  • Convolutional neural network
  • FMCW MIMO radar
  • Human form point cloud
  • Human posture and shape
  • Vital sign estimation
  • 2024 OA procedure


Dive into the research topics of 'Enhancing Vital Sign Estimation Performance of FMCW MIMO Radar by Prior Human Shape Recognition'. Together they form a unique fingerprint.

Cite this