About

Pilwon Hur is a leading researcher in the field of robotic rehabilitation and assistive technologies, with a primary focus on human gait analysis and the control of lower-limb prostheses and exoskeletons. His most impactful work centers on developing intelligent, data-driven methods for continuous gait phase estimation, a critical component for achieving seamless, intuitive control of robotic limbs. His 2021 paper on using Long Short-Term Memory (LSTM) networks for this purpose has garnered 67 citations, demonstrating its significance in enabling speed-adaptive control for transfemoral prostheses. Hur has also made notable contributions to generating human-like walking patterns, employing optimization-based spline generation for upslope walking and trajectory optimization for bipedal robots. Beyond locomotion, his research extends to human-robot interaction, exploring the use of haptic and visual feedback to improve performance in assistive interfaces and balance rehabilitation tools. His work on a wearable skin stretch device for interactive balance training highlights a commitment to practical, user-centered design. Through these diverse contributions, Hur is advancing the state-of-the-art in creating more responsive and natural-feeling robotic aids for individuals with mobility impairments.

Research Focus

Key Achievements

6
H-Index
10
Papers
144
Total Citations
14
Avg Citations/Paper
🏆 Most Cited Paper
Continuous Gait Phase Estimation Using LSTM for Robotic Transfemoral Prosthesis Across Walking Speeds
67 citations · 2021
📈 Most Prolific Year: 2017 (3 Papers)
🤝 Key Collaborators: 16
🏛 Institutions: Gwangju Institute of Science and Technology, Texas A&M University, University of North Carolina at Chapel Hill

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago