Wanbing Song

Hefei University of Technology

Papers

2

Total Citations

5

H-Index

2

About

Wanbing Song is a researcher focused on advancing rehabilitation robotics, with key contributions in the design and motion synthesis of assistive devices for both upper and lower limb therapy. Their work centers on developing cost-effective, accessible solutions for patient-specific rehabilitation, addressing the critical need for personalized and community-adaptable robotic systems. Song’s notable research includes a 2019 study on motion synthesis for upper-limb rehabilitation, which employs a clustering-based machine learning method to generate individualized motion patterns tailored to diverse patient body parameters—a significant step toward adaptive therapy. In a 2020 study, they designed a 1-degree-of-freedom robot with a humanoid gait for lower limb rehabilitation, utilizing a Watt-I six-bar mechanism to achieve a simple, low-cost structure suitable for home and community use, countering the complexity and expense of conventional devices. While their citation counts (3 and 2, respectively) reflect an emerging impact, these works demonstrate a clear commitment to bridging engineering innovation with clinical practicality, laying groundwork for scalable rehabilitation technologies. Song’s achievements highlight a promising trajectory in making robotic therapy more personalized and widely available.

Research Focus

Key Achievements

2
H-Index
2
Papers
5
Total Citations
3
Avg Citations/Paper
🏆 Most Cited Paper
Motion Synthesis for Upper-Limb Rehabilitation Motion With Clustering-Based Machine Learning Method
3 citations · 2019
📈 Most Prolific Year: 2019 (1 Papers)
🤝 Key Collaborators: 7
🏛 Institutions: Hefei University of Technology

Top Papers

  1. 1
  2. 2

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago