@inproceedings{806c3d65d14941a99b103a929c6bd93e,
title = "Deep Learning-Based Identification of Random Body Movements for Enhanced RF Sensing",
abstract = "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.",
keywords = "Deep learning, health monitoring, radio frequency sensing, remote sensing, respiration monitoring, vital signs monitoring",
author = "Prisila Ishabakaki and Muhammad Farooq and Hira Hameed and Michael Mollel and Hasan Abbas and Imran, \{Muhammad Ali\} and Qammer Abbasi",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 4th IEEE Wireless, Antenna and Microwave Symposium, WAMS 2025 ; Conference date: 05-06-2025 Through 08-06-2025",
year = "2025",
doi = "10.1109/WAMS64402.2025.11158008",
language = "English",
series = "4th Wireless, Antenna and Microwave Symposium, WAMS 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "4th Wireless, Antenna and Microwave Symposium, WAMS 2025",
address = "United States",
}