We propose the design, implementation, and evaluation of an instantaneous angular speed (IAS) measurement system, namely Wi-Rotate, using commercial-off-the-shelf (COTS) WiFi hardware. Wi-Rotate exploits the Channel State Information (CSI) of WiFi signals to extract the physical characteristics of the rotation object to achieve accurate contact-free IAS measurements. Wi-Rotate contains three main components: Wi-Fresnel model, Wi-Phase model, and a combination model. Wi-Fresnel model explores the signal amplitude variation features when the rotating object cuts the Fresnel zone boundary to track target rotation. Wi-Phase model leverages signal phase variation and formalizes the problem of determining IAS as a linear programming problem. The combination model combines the IAS values obtained by Wi-Fresnel and Wi-Phase and utilizes a clustering method to further improve measurement accuracy. Comprehensive experiments are conducted to demonstrate the advantages of Wi-Rotate in terms of accuracy, sensing range, and system latency. Wi-Rotate is able to achieve real-time rotation measurements at an accuracy higher than 99% when the target is within 2 meters. Even when the target is 3 meters away, Wi-Rotate can still achieve an accuracy of 94%, demonstrating the long-range tracking capability which is critical for industrial applications.

The ubiquitous and fine-grained features of WiFi signals make it promising for realizing contactless authentication. Existing methods, though yielding reasonably good performance in certain cases, are suffering from two major drawbacks: sensitivity to environmental dynamics and over-dependence on certain activities. Thus, the challenge of solving such issues is how to validate human identities under different environments, even with different activities. Toward this goal, in this article, we develop WiTL, a transfer learning–based contactless authentication system, which works by simultaneously detecting unique human features and removing the environment dynamics contained in the signal data under different environments. To correctly detect human features (i.e., human heights used in this article), we design a Height EStimation (HES) algorithm based on Angle of Arrival (AoA). Furthermore, a transfer learning technology combined with the Residual Network (ResNet) and the adversarial network is devised to extract activity features and learn environmental independent representations. Finally, experiments through multi-activities and under multi-scenes are conducted to validate the performance of WiTL. Compared with the state-of-the-art contactless authentication systems, WiTL achieves a great accuracy over 93% and 97% in multi-scenes and multi-activities identity recognition, respectively.

Human authentication is critical to protect personal and property security. Existing contactless authentication methods face some drawbacks, such as requiring large data size and low accuracy in cross-domain recognition, which hinders widespread popularization in practical applications. In this paper, we design and implement WiLCA, a WiFi-based lightweight contactless authentication system. First, we devise a Channel State Information (CSI) stream selection scheme to extract human movement features and reduce the sample size in the recognition process. Then, an AGO model is proposed, in which a Siamese Neural Network (SNN) framework with a cross-entropy module is used to guarantee accurate human authentication with limited data, and a lightweight GhostNet accelerates authentication with cheap operations. At last, extensive experiments are conducted to demonstrate the advantages of WiLCA, revealing that compared with state-of-the-art methods, WiLCA can reduce the data size by at least 2.5 x and achieve accurate authentication with an accuracy of over 98%.