WiCare: Towards In-Situ Breath Monitoring

Respiratory conditions significantly impact the health of individuals in the modern society. Long-term breath monitoring is critical for diagnosing the onset of various chronic respiratory diseases. Traditional breathing monitoring methods rely on wearable devices (e.q. face masks or chest bands) which are intrusive and uncomfortable. Recent research has demonstrated that it is possible to use device-free WiFi sensing to monitor breathing. However, these approaches only work when the monitored individual is stationary, i.e., sleeping or sitting perfectly still. In this paper, we propose WiCare, a system that employs the off-the-shelf WiFi devices and is able to monitor in-situ breathing rate in a natural setting where the individual can perform actions such as reading, writing, using phone, etc, which we refer to as micro motions. WiCare exploits Channel State Information (CSI) of WiFi data and can effectively distinguish breathing from the micro motions performed by the monitored individuals. The key idea is that certain specific subcarriers carry strong imprints of breathing motions because of the multipath effect and frequency and spacial diversity of MIMO systems. We model breathing signals as periodical sinusoidal waves and use curve fitting realised by interior point non-linear optimisation to identify breath in time series of each subcarrier. The goodness of fit measured by Dynamic Time Warping is exploited to select subcarriers that effectively capture breathing. Independent component analysis is used to precisely isolate the breathing signals. We recruit five participants to perform 9 common micro motions. Our extensive experiments show WiCare can accurately distinguish breathing from the micro motions and estimate breath rate with an average accuracy of over 90%. WiCare also outperforms the state-of-the-art breath rate estimation methods by up to 80%. WiCare represents a first and important step towards in-situ breath monitoring in natural settings.