부산대학교 도서관

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 article, 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.