@article { , title = {Pushing the limits of remote RF sensing by reading lips under the face mask}, abstract = {The problem of Lip-reading has become an important research challenge in recent years. The goal is to recognise speech from lip movements. Most of the Lip-reading technologies developed so far are camera-based, which require video recording of the target. However, these technologies have well-known limitations of occlusion and ambient lighting with serious privacy concerns. Furthermore, vision-based technologies are not useful for multi-modal hearing aids in the coronavirus (COVID-19) environment, where face masks have become a norm. This paper aims to solve the fundamental limitations of camera-based systems by proposing a radio frequency (RF) based Lip-reading framework, having an ability to read lips under face masks. The framework employs Wi-Fi and radar technologies as enablers of RF sensing based Lip-reading. A dataset comprising of vowels A, E, I, O, U and empty (static/closed lips) is collected using both technologies, with a face mask. The collected data is used to train machine learning (ML) and deep learning (DL) models. A high classification accuracy of 95\% is achieved on the Wi-Fi data utilising neural network (NN) models. Moreover, similar accuracy is achieved by VGG16 deep learning model on the collected radar-based dataset.}, doi = {10.1038/s41467-022-32231-1}, issue = {1}, journal = {Nature Communications}, publicationstatus = {Published}, publisher = {Nature Publishing Group}, url = {http://researchrepository.napier.ac.uk/Output/2913691}, volume = {13}, year = {2024}, author = {Hameed, Hira and Usman, Muhammad and Tahir, Ahsen and Hussain, Amir and Abbas, Hasan and Cui, Tie Jun and Imran, Muhammad Ali and Abbasi, Qammer H.} }