Peisen Wang

University of Science and Technology of China

Papers

Total Citations

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About

Peisen Wang is a researcher whose work lies at the intersection of human-computer interaction, robotics, and machine learning, with a particular focus on gesture recognition and motion analysis. His most cited work, "A Kinect based gesture recognition algorithm using GMM and HMM" (2013, 25 citations), introduced a novel approach that leverages Microsoft Kinect’s 3D joint-tracking capabilities. By combining Gaussian Mixture Models (GMM) for feature representation and Hidden Markov Models (HMM) for temporal modeling, Wang developed a robust framework for recognizing full-body gestures—a critical capability for intuitive human-robot interaction and immersive virtual environments. This contribution stands as a foundational reference in the field, demonstrating how affordable depth sensors can be paired with probabilistic models to achieve reliable gesture classification. Wang’s research addresses a key challenge in making technology more responsive and natural to human input, bridging the gap between raw sensor data and meaningful action recognition. His work continues to influence subsequent studies in assistive robotics, gaming, and rehabilitation, showcasing the enduring value of combining statistical learning with real-world sensing to advance human-centered computing.