Multi-User Human-Interaction through WiFi Base

The dataset accompanying this research endeavor serves as a valuable resource for scholars and researchers engaged in the domain of human activity recognition using WiFi signals, with a specific focus on multi-people sensing. The dataset was meticulously curated through experimentation conducted with a Raspberry Pi 4B setup, employing two distinct devices (RPi1, RPi2). The data collection spanned diverse scenarios and activities, encapsulating the complexities inherent in multi-user environments. The dataset comprises CSI data, extracted using MATLAB code from TCPDUMP files, and subsequently subjected to a rigorous preprocessing and cleaning pipeline. This meticulous approach ensures the integrity and quality of the dataset, providing a robust foundation for subsequent analyses and experimentation. In terms of experimental design, the dataset encapsulates a myriad of scenarios, each denoted by specific nomenclature (e.g., fx_S_2_1, fx_S_3_2), elucidating the nature of activities performed by individuals in a given environment. The experimental setup includes considerations for room dimensions (9m x 13m x 30cm), device placement (3m distance, 1m height for transmitter, 1.5m for receivers), and various activities such as standing, walking, sitting, running, and falls. The dataset's academic significance lies in its capacity to facilitate in-depth investigations into the challenges and potentials of multi-people sensing using WiFi signals, particularly through the lens of Channel State Information. Researchers can leverage this dataset to validate and extend existing methodologies, fostering advancements in the realm of WiFi-based human activity recognition.