Towards Device-Free Cross-Scene Gesture Recognition from Limited Samples in Integrated Sensing and Communication

Device-free gesture recognition (DFGR) is a critical technology for human-computer interaction and can be used for applications such as smart homes, and virtual reality in Wi-Fi sensing. Existing deep learning-based DFGR techniques typically require a large amount of labeled sensing data and are sensitive to the scene, which limits the development of ubiquitous sensing. In this study, in order to achieve device-free cross-scene gesture recognition from limited sensing samples, we consider the use of a small amount of Wi-Fi channel status information (CSI) data that are continuously obtained from low-cost commercial Wi-Fi devices. To this end, a few-shot learning-based cross-scene DFGR model is proposed for capturing highly discriminative information from dynamic CSI sequences. This information is then used to distinguish different gestures in the limited samples. Our experimental results using Wi-Fi signal collected at real world show that our model is able to realize 99.52% accuracy and can work well even with only one-piece data of new scene.