Recent advancements in Wi-Fi-based sensing technologies have enabled effective hand gesture recognition. However, most studies focus on single gesture recognition and fail to recognize naturally performed continuous gestures without pauses in transitions. The main challenges include diverse and uncertain transitions in continuous gesture recognition, making it difficult to segment and identify gestures from a stream of continuous hand movements. In this paper, we introduce a new method to recognize continuously performed gestures from a set of predefined gestures (e.g., digits) without requiring a pause in transitions. Instead of segmenting gestures at the gesture-transition level, we segment the stream into basic fractions that depict exclusive moving patterns of gestures. We propose a novel feature called meta motion, which geometrically characterizes different basic hand movements. Leveraging this feature, we use a back-tracking searching-based algorithm to identify gestures from the sequence of meta motions. Based on this approach, we develop a prototype system, WiCGesture, on commodity Wi-Fi devices. WiCGesture is the first system engaging in continuous gesture recognition using Wi-Fi signals. Evaluation results show that WiCGesture effectively recognizes continuous gestures from two gesture sets, significantly outperforming state-of-the-art methods.

WiFi-based device-free motion tracking systems track persons without requiring them to carry any device. Existing work has explored signal parameters such as time-of-flight (ToF), angle-of-arrival (AoA), and Doppler-frequency-shift (DFS) extracted from WiFi channel state information (CSI) to locate and track people in a room. However, they are not robust due to unreliable estimation of signal parameters. ToF and AoA estimations are not accurate for current standards-compliant WiFi devices that typically have only two antennas and limited channel bandwidth. On the other hand, DFS can be extracted relatively easily on current devices but is susceptible to the high noise level and random phase offset in CSI measurement, which results in a speed-sign-ambiguity problem and renders ambiguous walking speeds. This paper proposes WiTraj, a device-free indoor motion tracking system using commodity WiFi devices. WiTraj improves tracking robustness from three aspects: 1) It significantly improves DFS estimation quality by using the ratio of the CSI from two antennas of each receiver, 2) To better track human walking, it leverages multiple receivers placed at different viewing angles to capture human walking and then intelligently combines the best views to achieve a robust trajectory reconstruction, and, 3) It differentiates walking from in-place activities, which are typically interleaved in daily life, so that non-walking activities do not cause tracking errors. Experiments show that WiTraj can significantly improve tracking accuracy in typical environments compared to existing DFS-based systems. Evaluations across 9 participants and 3 different environments show that the median tracking error$<2.5\%$for typical room-sized trajectories.