Papers
2
Total Citations
11
H-Index
2
About
Donggon Jang is a leading researcher at the intersection of rehabilitation robotics and artificial intelligence, with key contributions in wearable robot-assisted gait training (RAGT) and self-supervised learning for anomaly detection. His work on RAGT demonstrates how high-intensity, task-specific training can improve gait function in elderly adults and patients with gait disorders, while also exploring the neuroplastic changes underlying these improvements—a preliminary study (2024) has already garnered 7 citations for its clinical relevance. In parallel, Jang has advanced AI methodologies with a generality-aware self-supervised transformer for multivariate time series anomaly detection (2025, 4 citations), addressing critical challenges in real-world monitoring systems. His research uniquely bridges rehabilitation engineering and machine learning, offering both practical therapeutic tools and robust analytical frameworks. Jang’s interdisciplinary approach positions him as a rising figure in translational research, with his work cited for its potential to enhance patient outcomes through adaptive robotics and intelligent data analysis.
Research Focus
Key Achievements
Top Papers
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