We propose a non-contact, privacy-preserving emotion recognition framework using millimeter-wave (mm-Wave) radar and deep learning, addressing the limitations of traditional wearable and camera-based approaches. By broadcasting frequency-modulated radar pulses, the system isolates heart rate signals even in dynamic scenarios such as gameplay Fig. 1. The design integrates a hybrid 1D-CNN for efficient feature extraction and Bi-LSTM for temporal analysis, with a computational complexity of O(N · F + N · H), ensuring real-time capability. Validation through ROC curves, alongside F1-scores and precision-recall metrics ranging from 0.98 to 0.99, confirms the system's reliability. Unlike existing methods, this framework investigates the robustness of mm-wave radar to function independently of environmental factors like lighting or clothing, making it scalable for applications in healthcare, human-computer interaction, and educational settings. These findings establish mm-wave radar as a transformative tool for emotion recognition, offering enhanced comfort, privacy, and adaptability.
