Alexander Osipov

Papers

Total Citations

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About

Alexander Osipov is a researcher advancing the field of human-computer interaction (HCI) through innovative work in gesture recognition and machine learning. His primary research areas include real-time hand gesture recognition, skeleton-based analysis, and the integration of lightweight frameworks for practical HCI applications. Osipov’s most cited work, "Real-time static custom gestures recognition based on skeleton hand" (2021, 10 citations), introduces a novel approach that leverages the MediaPipe framework and Support Vector Machine (SVM) classification to accurately recognize static hand gestures in real time. This contribution is significant because it enhances natural, intuitive interaction between humans and computers, making gesture-based control more accessible and efficient. By focusing on skeleton-based hand tracking, Osipov’s method reduces computational complexity while maintaining high recognition accuracy, a crucial step toward deploying gesture recognition in everyday devices. His work has been recognized for its practical impact, offering a scalable solution for custom gesture sets. Osipov continues to explore the intersection of computer vision and machine learning, aiming to refine HCI systems that are both responsive and user-friendly.