Authors - Subham Ghosh, Banani Basu, Arnab Nandi Abstract - Radio-frequency based human activity recognition (HAR) using wearable antennas has recently gained interest due to its promise for comfortable and effective monitoring in applications such as smart healthcare and surveillance. However, traditional deep learning (DL) models for HAR are often constrained due to their reliance on large datasets and poor generalization performance. This paper presents an innovative framework for capturing and recognizing two-hand movements by using the near-field of a wearable antenna. The proposed system innovatively integrates signal smoothing, Morlet wavelet transform (MWT) time-frequency (TF) transformation, feature extraction based on statistical significance using the Kruskal-Wallis test, and a quantum artificial neural network (QANN) for robust feature learning and classification. The performance of the suggested technique is systematically compared against traditional machine learning models. Experimental results demonstrate that the proposed framework achieves superior classification performance for hand activity identification, underscoring its efficacy and promise for wearable RF-based HAR systems.
