Taehyun Lim

Papers

Total Citations

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About

Taehyun Lim is a leading researcher in neural engineering and human-machine interaction, with a primary focus on developing advanced myoelectric interfaces for assistive robotics. His most cited work, "Upper-Limb Electromyogram Classification of Reaching-to-Grasping Tasks Based on Convolutional Neural Networks for Control of a Prosthetic Hand" (2021, 23 citations), represents a significant breakthrough in decoding upper-limb movement intentions from surface electromyogram (EMG) signals. Lim pioneered the application of convolutional neural networks to classify complex reaching-to-grasping tasks, directly addressing the critical challenge of intuitive prosthetic hand control for individuals with physical disabilities. His research bridges the gap between raw neural signals and precise robotic actuation, enabling more natural and responsive prosthetic devices. By demonstrating that deep learning architectures can effectively interpret the nuanced patterns of muscle activity during functional movements, Lim has laid essential groundwork for next-generation myoelectric interfaces. His contributions are particularly impactful for the development of assistive technologies that restore motor function and improve quality of life for amputees and individuals with upper-limb impairments. Lim’s work continues to influence both the academic community and practical prosthetic design, marking him as a key innovator in neural decoding for rehabilitation engineering.