Junho Maeng
Papers
1
Total Citations
35
H-Index
1
About
Junho Maeng is a leading researcher in the field of human-robot interaction and assistive robotics, with a primary focus on the development of intelligent, adaptive control systems for lower-limb power-assist devices. His work is centered on the critical challenge of intuitive and reliable human-machine interfaces, particularly through the integration of biosignal processing and machine learning. Maeng’s most notable contribution is a pioneering approach to gait sub-phase detection and prediction, which synergistically fuses surface electromyogram (sEMG) signals, inertial measurement unit (IMU) data, and pressure sensors. His 2019 paper on this topic, which has garnered 35 citations, directly addresses the inherent instability of sEMG-based pattern recognition by proposing a user-adaptive classifier that significantly improves the robustness and real-time responsiveness of powered exoskeletons. This work is foundational for enabling seamless, natural movement assistance, directly impacting the design of next-generation rehabilitation and mobility aids. By tackling the core problem of sensor fusion and user-specific adaptation, Maeng’s research is paving the way for more practical and effective assistive technologies that can adapt to the unique gait patterns of individual users.
Research Focus
Key Achievements
Top Papers
- 1