# Sparsity Based Non-Contact Vital Signs Monitoring of Multiple People Via FMCW Radar

**Authors:** Yonathan Eder, Yonina C. Eldar  
**arXiv:** [2205.05152](https://arxiv.org/abs/2205.05152)  
**Date:** 2022-05-10  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2205.05152v1/

## Key points

- Non-contact technology for monitoring vital signs of multiple people is important due to increasing prevalence of cardiopulmonary diseases, risk of transmitting infections, and heavy workload on medical staff
- Frequency modulated continuous wave (FMCW) radars have potential in meeting these needs
- Contemporary techniques for non-contact vital signs monitoring via FMCW radars face challenges in noisy environments with multiple objects
- Yonathan Eder and Yonina C. Eldar developed an extended model of FMCW radar signals that can accurately localize humans and monitor their vital signs even in cluttered settings with multiple people using a single channel and a single-input-single-output setup
- The authors utilize the sparse nature of modeled signals along with human-typical cardiopulmonary features to detect multiple people accurately and efficiently extract their Doppler samples through a joint sparse recovery approach
- The authors introduce a method named Vital Signs based Dictionary Recovery (VSDR), which uses a dictionary-based approach to search for desired rates of respiration and heartbeat over high-resolution grids corresponding to normal cardiopulmonary activity given the extracted samples
- Through examples combining real data from 30 monitored individuals with the proposed model, the authors demonstrate accurate human localization in clutter-rich scenarios that include both static and vibrating objects
- The VSDR approach outperforms existing techniques based on several statistical metrics
- FMCW radars with proposed algorithms provide an efficient way to monitor vital signs without contact while reducing risks associated with traditional methods such as physical contact or exposure to radiation
- The proposed method could potentially revolutionize how medical professionals monitor patients' health by providing an accurate, non-invasive alternative that is easy to implement in various healthcare settings.

## Summary

Non-contact technology for monitoring vital signs of multiple people has gained importance in recent years due to the increasing prevalence of cardiopulmonary diseases, the risk of transmitting infections, and the heavy workload on medical staff. Frequency modulated continuous wave (FMCW) radars have shown great potential in meeting these needs. However, contemporary techniques for non-contact vital signs monitoring (NCVSM) via FMCW radars are based on simplistic models and face challenges in noisy environments containing multiple objects. To address this issue, Yonathan Eder and Yonina C. Eldar have developed an extended model of FMCW radar signals that can accurately localize humans and monitor their vital signs even in cluttered settings with multiple people using a single channel and a single-input-single-output setup. The authors utilize the sparse nature of modeled signals along with human-typical cardiopulmonary features to detect multiple people accurately and efficiently extract their Doppler samples through a joint sparse recovery approach. The authors then introduce a method named Vital Signs based Dictionary Recovery (VSDR), which uses a dictionary-based approach to search for desired rates of respiration and heartbeat over high-resolution grids corresponding to normal cardiopulmonary activity given the extracted samples. Through examples combining real data from 30 monitored individuals with the proposed model, the authors demonstrate accurate human localization in clutter-rich scenarios that include both static and vibrating objects. They also show that their VSDR approach outperforms existing techniques based on several statistical metrics. These findings support widespread use of FMCW radars with proposed algorithms in healthcare as they provide an efficient way to monitor vital signs without contact while reducing risks associated with traditional methods such as physical contact or exposure to radiation. The proposed method could potentially revolutionize how medical professionals monitor patients' health by providing an accurate, non-invasive alternative that is easy to implement in various healthcare settings.