The developed Wi-MIR dataset is pivotal to Wi-Fi-based human activity recognition, specifically for multi-person interactions (MIPs) recognition. When humans move in between the transmitting and receiving antennas, the movement of various body parts in between Wi-Fi signals’ propagation path generates changes in the signal reflections and refraction, which is evident by the channel state information (CSI) variations.  

This dataset is collected within an indoor setting, specifically a furnished room measuring 8m x 6.1m, consisting of seventeen multi-person interactions (MPIs) conducted by eleven human pairs utilizing Wi-Fi. Each pair of subjects performed twenty trials of each of the seventeen MPIs, and the total number of 3,740 trials is recorded in our dataset  (i.e., 17 interactions  × 11 pairs of human ×  20 trials). The publicly available CSI tool records the Wi-Fi signals transmitted from a Wi-Fi router (NETGEAR Nighthawk-R7000) operating as an AP to a desktop computer equipped with an intel 5300 Network Interface Card (NIC) operating as a receiver. The AP and receiver have three transmitting and receiving antennas, respectively and operate at a 5 GHz frequency band, specifically on channel number 36, with a channel bandwidth of 20 MHz. The recorded Wi-Fi signals consist of the Received Signal Strength Indicator (RSSI) and the Channel State Information (CSI) values. Our dataset can be exploited to advance Wi-Fi-based human activity recognition in different aspects, such as machine learning and Deep learning algorithms to recognize different MPIs.