Papers
2
Total Citations
46
H-Index
2
About
Yajing Guo’s research lies at the intersection of precision mechanical systems and human–robot interaction, with key contributions to harmonic gear reducers and lower-limb exoskeleton control. In their most-cited work, “Analysis of Contact Mechanical Characteristics of Flexible Parts in Harmonic Gear Reducer” (2021, 30 citations), Guo investigates the contact mechanics of thin-walled flexible bearings and flexsplines—critical components whose failure often limits the lifespan of harmonic drives used in industrial robots, aerospace, and optics. This work provides foundational insights for improving durability in high-precision transmission systems. Guo also advances assistive robotics through “Multi-Channel sEMG Based Human Lower Limb Motion Intention Recognition Method” (2019, 16 citations), which introduces a cepstrum distance-based endpoint detection technique for surface electromyography signals. This method enhances the accuracy of decoding user intent for exoskeleton control, directly addressing challenges in real-time human–machine collaboration. Together, Guo’s research bridges mechanical reliability and intelligent control, offering practical solutions for both industrial automation and rehabilitation robotics. Their work continues to influence the design of more robust and responsive robotic systems.
Research Focus
Key Achievements
Top Papers
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