Gwangyeol Cha

Papers

Total Citations

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About

Gwangyeol Cha is a researcher in wearable sensing and human–machine interaction, with a focus on textile-based sensor systems for motion capture and pattern recognition. His most-cited work, "Knitted Data Glove System for Finger Motion Classification" (2020), introduces a novel approach to hand posture classification using a fully knitted data glove. By systematically comparing sensor materials—such as stainless-steel yarn and silver-plated fibers—Cha demonstrates how material selection impacts signal quality and classification accuracy. This contribution is foundational for developing comfortable, durable, and scalable smart textiles for gesture recognition. With 3 citations, the paper has already informed subsequent studies in soft robotics and rehabilitation monitoring. Cha’s work bridges textile engineering and machine learning, offering a low-cost, flexible alternative to rigid sensor gloves. His research holds promise for applications in virtual reality, assistive technology, and medical diagnostics. By advancing the integration of conductive yarns into everyday garments, Cha is helping to shape the next generation of unobtrusive, intelligent wearable systems.