mm-HrtEMO: Non-Invasive Emotion Recognition via Heart Rate Using mm-Wave Sensing in Diverse Scenarios

• Published in: IEEE journal of biomedical and health informatics 2024-12, p.1-12
• Main Authors: Imran, Naveed, Zhang, Jian, Ali, Jehad, Hameed, Sana, Younas, Muhammad, hanif, Danial, Alenazi, Mohammed J.F., Niaz, Fahim
• Format: Article
• Language: English
• Subjects: Bioinformatics; Biomedical monitoring; Deep Learning; Emotion recognition; Feature extraction; Frequency measurement; Health Information; Heart beat; Heart rate; Human-Computer Interaction; Millimeter Wave Radar; Monitoring; Radar; Radar detection
• ISSN: 2168-2194; 2168-2208
• DOI: 10.1109/JBHI.2024.3522316

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 \cdot F + N \cdot 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.