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About

M. Murugappan is a leading researcher in affective computing and human-robot interaction, whose work bridges signal processing, machine learning, and real-world applications. His core contributions lie in emotion recognition using both physiological signals and facial expressions. He pioneered the classification of human emotions from EEG signals using wavelet transforms and K-nearest neighbors (86 citations), and advanced facial emotion recognition through geometric feature extraction and machine learning (67 citations). Murugappan also explored emotion detection from electrocardiogram signals using Hilbert-Huang Transform (22 citations), demonstrating the reliability of bio-signal-based methods for human-computer interaction. Beyond emotion, he developed a hospital nurse-following robot (15 citations), showcasing his commitment to practical robotics. His recent work extends to deep learning for agricultural automation, using YOLOv8 on Raspberry Pi for real-time tomato ripeness detection (10 citations), and novel partitioned random forest methods for facial emotion recognition (2 citations). With over 200 total citations, Murugappan’s research consistently integrates theoretical innovation with deployable systems, making him a key figure in creating emotionally intelligent and assistive technologies.