WiFi-Based Real-Time Breathing and Heart Rate Monitoring during Sleep

Abstract

Good quality sleep is essential for good health and sleep monitoring becomes a vital research topic. This paper provides a low cost, continuous and contactless WiFi-based vital signs (breathing and heart rate) monitoring method. In particular, we set up the antennas based on Fresnel diffraction model and signal propagation theory, which enhances the detection of weak breathing/heartbeat motion. We implement a prototype system using the off-shelf devices and a real-time processing system to monitor vital signs in real time. The experimental results indicate the accurate breathing rate and heart rate detection performance. To the best of our knowledge, this is the first work to use a pair of WiFi devices and omnidirectional antennas to achieve real-time individual breathing rate and heart rate monitoring in different sleeping postures.

Wital: Contactless Real-time Vital Signs Monitoring System Using COTS WiFi Devices

“…Note that this paper is an extension of our previous work [40]. In [40], we found that blocking the LOS signal is beneficial to NLOS sensing, and in this paper, we use the Ricean-K theory-based model to explain why. We also propose a new motion segmentation algorithm based on regularity detection, which can accurately locate the sleep motions (such as turn over and get up) different from breathing/heartbeat.…”

“…We compare these systems and our work in Table I. Note that this paper is an extension of our previous work [40]. In [40], we found that blocking the LOS signal is beneficial to NLOS sensing, and in this paper, we use the Ricean-K theory-based model to explain why.…”

“…Therefore, it is essential to choose a proper subcarrier that can better capture vital signs. According to the previous experience [40], we choose the subcarrier with the biggest variance for Wital. Motion Segmentation.…”

Wital: A COTS WiFi Devices Based Vital Signs Monitoring System Using NLOS Sensing Model

“…Note that this paper is an extension of our previous work [39]. In this paper, we use Rice-K theory beside Fresnel theory to enhance the detection accuracy.…”

“…Therefore, it is essential to choose proper subcarrier that can better capture the breath. According to previous experience [39], we choose the subcarrier with the most significant variance. and heartbeat and make it difficult to separate them.…”