Wonho Heo
Papers
1
Total Citations
112
H-Index
1
About
Wonho Heo has made significant contributions to the field of assistive robotics, with a primary focus on enhancing the control and usability of lower limb exoskeleton robots. His most cited work, "A Neural Network-Based Gait Phase Classification Method Using Sensors Equipped on Lower Limb Exoskeleton Robots" (2015, 112 citations), addresses a critical challenge in exoskeleton technology: accurately classifying different phases of human gait to enable seamless, intuitive control. By leveraging neural networks and sensor signals from foot sensors, Heo developed a method that allows exoskeletons to better detect user intentions, improving both safety and mobility for individuals with lower limb impairments. This research has had a notable impact, serving as a foundational reference for subsequent studies in gait analysis and human-robot interaction. Heo’s work exemplifies the integration of machine learning with mechanical design, pushing the boundaries of how robotic systems can adapt to human movement. His contributions are particularly valuable for students and researchers interested in rehabilitation engineering, wearable robotics, and the practical application of neural networks in real-world robotic systems.
Research Focus
Key Achievements
Top Papers
- 1