convolutional neural network based human activity recognition using csi

Human activity recognition (har) has the potential to significantly impact applications such as health monitoring, context-aware systems, transportation, robotics, and smart cities. because of the prevalence of wireless devices, the wi-fi-based approach has attracted a lot of attention among other existing methods such as sensor-based and vision-based har. wi-fi devices can be used to distinguish between daily activities such as walking, running, and sleeping, which affect wi-fi signal propagation. this paper proposes a deep learning method for har tasks that makes use of channel state information (csi). we convert the csi data to rgb images and classify the activity recognition using a 2d-convolutional neural network (cnn). we evaluate the performance of the proposed method on two publicly available datasets for csi data. our experiments show that converting data into rgb images improves performance and accuracy compared to our previous method by at least 5%.