High Accuracy WiFi Sensing for Vital Sign Detection with Multi-Task Contrastive Learning

Abstract

WiFi sensing has emerged as a promising technique in the healthcare industry, enabling contact-free monitoring of vital signs by detecting changes in WiFi signals resulting from physiological activities. State-of-the-art WiFi sensing uses channel state information (CSI) to analyze signal characteristics, capturing subtle changes due to heartbeats and breathing. However, existing methods face challenges in concurrently measuring respiration and heart rates, and they exhibit high sensitivity to environmental factors and individual differences, limiting the detection accuracy of a trained model in real-world environments.

In this paper, we propose a novel multi-task contrastive learning framework for concurrent detection of respiration and heart rates. We introduce multi-task learning with hard-shared layers to exploit the physiological link between breathing and heartbeat. Additionally, we leverage contrastive learning to improve our model's ability to differentiate and prioritize CSI changes related to respiratory and cardiac activities.

The experimental results demonstrate the proposed model's ability to accurately measure respiratory and heart rates in challenging scenarios, including long-distance and non-line-of-sight conditions, even when utilizing omnidirectional antennas.