Yong Un Jo

Papers

Total Citations

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About

Yong Un Jo is a researcher specializing in biomedical engineering and human-machine interaction, with a primary focus on electromyography (EMG)-based gesture recognition and its application to assistive technologies. His work centers on developing deep learning methods, particularly convolutional neural networks (CNNs), to accurately classify hand gestures from surface EMG signals. Jo’s major contributions include the integration of scale average wavelet transforms with CNNs for robust hand gesture classification, achieving high success rates in distinguishing grasping patterns. His most-cited paper (2019, 14 citations) demonstrates a novel approach to predicting human hand gestures for applications in active prosthetic hands, rehabilitation robots, and general artificial intelligence systems. A second key study (2019, 4 citations) further validates the use of deep learning for classifying grasp gestures, specifically targeting improvements in active prosthetics. Jo’s work addresses critical challenges in real-time, non-invasive control of prosthetic devices, offering potential to enhance the quality of life for amputees. His research stands at the intersection of signal processing, machine learning, and rehabilitation engineering, contributing to the broader field of intelligent human-robot interaction.