Imam Yogie Susanto

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About

Imam Yogie Susanto is a researcher at the forefront of affective computing and biomedical signal processing, with a particular focus on decoding human emotions through physiological signals. His most-cited work, "Emotion Recognition from Galvanic Skin Response Signal Based on Deep Hybrid Neural Networks" (2020), has garnered 26 citations and represents a significant contribution to the field. In this study, Susanto pioneered a novel framework that leverages deep hybrid neural networks to analyze Galvanic Skin Response (GSR) signals—electrical characteristics of human skin that reveal underlying emotional states. By integrating advanced deep learning architectures, his approach achieves more robust and accurate emotion classification compared to traditional methods, addressing the complex, non-linear nature of physiological data. This work has implications for human-computer interaction, mental health monitoring, and personalized affective technologies. Susanto’s research bridges the gap between raw biosignal data and meaningful emotional interpretation, offering a scalable solution for real-world applications. His contributions continue to inspire further exploration into non-invasive emotion recognition systems, positioning him as a rising voice in the intersection of machine learning and psychophysiology.