Prasertsak Tiawongsombat

Korea University of Science and Technology, King Mongkut's University of Technology North Bangkok

Papers

Total Citations

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About

Prasertsak Tiawongsombat is a researcher specializing in human-robot interaction, computer vision, and machine learning, with a particular focus on attention modeling and speakingness detection. His work addresses the critical challenge of enabling robots to perceive and respond to human attentiveness during communication. In his highly cited 2011 paper, "Robust visual speakingness detection using bi-level HMM," he introduced a bi-level Hidden Markov Model approach that achieved 29 citations for its robust detection of speaking activity from visual cues alone. Building on this foundation, his 2019 work "Vision-Based Attentiveness Determination Using Scalable HMM Based on Relevance Theory" proposed a novel framework that integrates relevance theory with scalable HMMs to allow robots to dynamically assess human attention. This contribution advances the field by moving beyond static attention models toward adaptive, context-aware systems. Tiawongsombat's research bridges theoretical frameworks from cognitive science with practical engineering solutions, offering significant implications for developing more natural and intuitive human-robot interactions. His work continues to influence the design of socially aware robotic systems capable of nuanced communication.