Contactless Body Gesture Recognition for Enhancing Non-Verbal Communication: A Deep Learning Approach Using RF Sensing

Deaf-mute individuals communicate through sign language, which involves hands and hand movements, body postures, and facial expressions. Despite advancements, recognizing sign language through automation continues to be a complex and emerging field of research. Current methods typically rely on sensor-based and vision-based approaches, both of which have limitations, such as privacy concerns, maintenance requirements, and sensitivity to ambient lighting conditions. Consequently, contactless sensing has emerged as a promising solution for recognizing automatic sign language. This study proposed a framework that used contactless sensing to recog-nise five specific gestures-Sad, Neutral, Fearful, Happy, and Surprised. A dataset of 150 samples is collected (each class is repeated 30 times). Afterthat, three pre-trained deep learning models: MobileNet, ResNet50, and VGGI6 are employed for the classification purpose. ResNet50 outperformed other models with 96% accuracy.