MIMO radar-based vital sign monitoring by slow-time over sampling

Mason Calderbank; Michael Jensen; Daniel Creighton; Daniel Smith; Syed A. Hamza

doi:10.1117/12.3013567

10 June 2024 MIMO radar-based vital sign monitoring by slow-time over sampling

Mason Calderbank, Michael Jensen, Daniel Creighton, Daniel Smith, Syed A. Hamza

Proceedings Volume 13036, Big Data VI: Learning, Analytics, and Applications; 130360K (2024) https://doi.org/10.1117/12.3013567
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

Traditional clinical vital sign measurement methods are often contact-based, causing discomfort for patients and practitioners and rendering it inconvenient for continuous monitoring. Additionally, close proximity during measurement poses the risk of disease transmission and allows only one patient to be monitored at a time. To address these challenges, contactless measurement methods are being explored, with radar technology emerging as a promising alternative for vital sign monitoring. The proposed design utilizes a MIMO radar system to remotely detect subtle chest movements caused by breathing and heartbeat. The primary challenge lies in separating weaker heartbeat movements from stronger breathing motions, in the presence of body movements which mask the chest movements due to vital signs. We employ filtering techniques and chirp averaging using slow-time oversampling to enable the precise estimation of breathing and heartbeat patterns. We collect radar vital sign data from various individuals with different resting heart rates in a controlled lab environment. The system’s performance is evaluated by comparing it with ground truth information obtained from pulse oximeter.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mason Calderbank, Michael Jensen, Daniel Creighton, Daniel Smith, and Syed A. Hamza "MIMO radar-based vital sign monitoring by slow-time over sampling", Proc. SPIE 13036, Big Data VI: Learning, Analytics, and Applications, 130360K (10 June 2024); https://doi.org/10.1117/12.3013567

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