In recent years, contactless sleep breath detection using WiFi signals has gained significant attention. In this paper, we present a position-free breath detection system that utilizes the channel state information (CSI) from a pair of WiFi devices. To address the “blind spot” issue, we propose a robust detection method that takes advantage of both the amplitude and phase of the CSI ratio, ensuring comprehensive coverage for breath detection. We also utilize the periodicity and variability features to select the most suitable breath pattern from the resulting signals. To mitigate the noise interference in non-ideal sleep positions, we propose a novel PCA-VMD fusion method to fully exploit the complementary advantage of sensing over different TX-RX pairs. In this way, we can extract the fine breath frequency component for the breath rate estimation. Extensive experiments are conducted to demonstrate the superiority of the proposed scheme to the existing state-of-the-arts.

By integrating sensing capability into wireless communication, wireless sensing technology has become a promising contactless and non-line-of-sight sensing paradigm to explore the dynamic characteristics of channel state information (CSI) for recognizing human behaviors. In this paper, we develop an effective device-free human gesture recognition (HGR) system based on WiFi wireless sensing technology in which the complementary CSI amplitude and phase of communication link are jointly exploited. To improve the quality of collected CSI, a linear transform-based data processing method is first used to eliminate the phase offset and noise and to reduce the impact of multi-path effects. Then, six different time and frequency domain features are chosen for both amplitude and phase, including the mean, variance, root mean square, interquartile range, energy entropy and power spectral entropy, and a feature selection algorithm to remove irrelevant and redundant features is proposed based on filtering and principal component analysis methods, resulting in the construction of a feature subspace to distinguish different gestures. On this basis, a support vector machine-based stacking algorithm is proposed for gesture classification based on the selected and complementary amplitude and phase features. Lastly, we conduct experiments under a practical scenario with one transmitter and receiver. The results demonstrate that the average accuracy of the proposed HGR system is 98.3% and that the F1-score is over 97%.