mm-HrtEMO: Non-Invasive Emotion Recognition via Heart Rate Using mm-Wave Sensing in Diverse Scenarios.
basic_science · Level V
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- Record sourced from PubMed, PMID 40030825.
- Also identified by DOI 10.1109/JBHI.2024.3522316.
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Abstract
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 , 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.