BUSCADOR RECOLECTA

In the field of WiFi Sensing (WS), developing applications requires data with quality, quantity, and variability to enhance cross-domain capability.This paper presents EHUNAM, a comprehensive channel state information (CSI) dataset developed for various WS applications, with a primary focus on people counting (PC), human activity recognition (HAR), and machine activity recognition (MAR), while remaining suitable for additional tasks. The dataset was acquired using diverse equipment configurations and under different scenarios, ensuring versatility and representativeness. Beyond traditional applications, EHUNAM includes measurements for recognizing activities of home appliances and industrial machines. To achieve high accuracy in new settings, data was collected over 23 days in eight distinct environments, including an industrial scenario, involving 21 people and nine machines that can also perform activities simultaneously. Validation using a convolutional neural network (CNN) for PC, HAR, and machine activity recognition (MAR), considering multiclass and multilabel classification, achieved over 90% accuracy in most cases, underscoring the dataset’s robustness and its capacity to tackle a broad spectrum of real-world scenarios., CONAHCYT through student grant No. 928123, the UNAM long-term activity support grants, the UNAM-PAPIIT project IN102025, the Basque Government under grant IT1436-22, and the Spanish Government through the THERESA project (grant PID2021-124706OB-I00, funded by MICIU/AEI/10.13039/501100011033 and ERDF, A way of making Europe).