WIMEETSENSE

Recent research has showcased the versatile potential of WiFi sensing across a spectrum of applications such as human activity recognition, gesture recognition,localization, human pose tracking, and numerous others. Among the most promising WiFi sensing techniques, Channel State Information (CSI)-based sensing stands out due to its capability of providing fine-grained measurement of WiFi Signals capturing human activities. Collecting a large-scale labeled CSI dataset across various users and environments is tedious and cumbersome; consequently, publicly available CSI datasets are scarce for application-specific HAR. Moreover, existingCSI datasets are collected primarily in controlled settings. In contrast, we present the first semi-controlled and in-the-wild CSI dataset WIMEETSENSE, collectedwith 33 participants in 5 different locations in 46 different experimental setups covering 51-hour sessions while they attend online meetings. Our dataset is collected utilizing 3 different modalities: WiFi CSI, participant’s video, and speaker’s audio, and is labeled with 7 head gesticulations.