Taeyang Gwon
Papers
2
Total Citations
7
H-Index
2
About
Taeyang Gwon is a researcher focused on advancing human-robot interaction and safety in robotic systems, with key contributions in exoskeleton technology and autonomous carrier robots. His work bridges the gap between human intent recognition and robotic actuation, particularly through the use of biosignal processing and artificial neural networks. In his most cited paper, "Real-Time Joint Torque Estimation on Embedded system using EMG and Artificial Neural Network for Exoskeleton Robot" (2020, 4 citations), Gwon developed a method to estimate joint torque in real time by integrating electromyography (EMG) signals with neural networks, enabling exoskeletons to respond more intuitively to user movements—a critical step toward practical, assistive wearable robots for industrial and rehabilitation settings. He also contributed to safety standardization with "A Study on Safety Evaluation Criteria of the Personal Carrier Robot Based on ISO 13482" (2019, 3 citations), where he designed a carrier robot platform with a dynamic dummy using a Stewart platform to simulate real-world loads and assess compliance with international safety norms. This work supports the growing deployment of social and logistics robots. Gwon’s research, though early in citation impact, addresses foundational challenges in embedded control and safety certification, laying groundwork for more reliable and responsive robotic systems.
Research Focus
Key Achievements
Top Papers
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