Filipino Sign Language Recognition Using Wi-Fi Channel State Information

Sign language recognition (SLR) systems often employ cameras and wearable sensors to detect and interpret signs and gestures. However, vision-based systems rely on LOS and may lead to privacy issues, while wearable systems require calibration and user contact. Alternatively, radio-frequency (RF) sensing, particularly Wi-Fi sensing, may be used for contactless NLOS SLR systems. While there have been some efforts in investigating different sign languages using RF systems, to our knowledge, none have explored the Filipino sign language (FSL) using Wi-Fi channel state information (CSI). In this study, we gathered CSI time series data from different participants performing FSL gestures one at a time using Wi-Fi devices. A total of 600 CSI samples were acquired and preprocessed using outlier removal, denoising and dimension reduction. Our Wi-Fi FSLR systems trained on 80% of the dataset was able to correctly classify up to 83 out of 120 test samples, that is 69.3% accuracy, showing the feasibility of using Wi-Fi CSI for FSLR.