Advances in Non-Invasive Emotion Recognition: A Review of ECG and Radar-Based Emotion Classifier Systems

Abstract

Emotion recognition has emerged as a critical area in human-computer interaction, mental health monitoring, and personalized healthcare. Many of the emotion classifier systems utilizes multimodal systems, lesser number of dingle modal systems are available in literature but with EEG signals. The acquisition of EEG signal is cumbersome but ECG signal acquisition is easier in comparison. Even with usage of mechanical movement of chest due to heartbeat can be translated into reconstruction of ECG signals, hence wireless acquisition of ECG is quite easier and employing single modal systems to come up with emotion classifier systems will be a promising field in integration of human emotion touch to modern AI based robotic systems. This review synthesizes recent developments focusing on electrocardiogram (ECG) signals and radar technologies for detecting emotional states through physiological responses. Key challenges, including signal noise reduction, accuracy in real-time scenarios, and multimodal fusion, are discussed. The analysis draws trends toward non-invasive, real-time systems with improved classification performance. The study also discusses current challenges and provides future directions for research.