Deep Learning-Based Identification of Random Body Movements for Enhanced RF Sensing

We present a deep learning (DL) approach for identifying random body movements (RBM) to enhance radio frequency (RF) sensing applications. The proposed method leverages a capsule neural network architecture to automate feature extraction, eliminating the need for manual feature engineering. This approach demonstrates robust performance by achieving an average RBM detection accuracy of 92% across diverse environments. The method enhances the accuracy and reliability of RF-based systems by mitigating RBM-induced interference, making it highly valuable for wireless sensing applications such as vital signs monitoring, facial recognition, and gesture detection.