Renjie Lv
Papers
1
Total Citations
4
H-Index
1
About
Renjie Lv is a researcher at the forefront of brain-computer interface (BCI) technology, specializing in neural decoding for rehabilitation robotics. His key research areas include EEG-based intention detection, dynamical connectivity analysis, and entropy-based signal processing. Lv’s major contribution lies in developing methods to decode human gait intentions—specifically sitting and standing—from brain signals, enabling more intuitive control of hybrid rehabilitation and intelligent walking aid robots. His 2023 work on "Sitting and Standing Intention Detection Based on Dynamical Region Connectivity and Entropy of EEG" (4 citations) introduces a novel framework that combines regional brain connectivity dynamics with entropy measures to improve classification accuracy of movement intentions. This approach addresses a core challenge in BCI systems: reliable, real-time prediction of action intent from noisy neural data. By bridging neural signal processing and assistive robotics, Lv’s research holds promise for enhancing the autonomy and responsiveness of exoskeletons and smart walkers, ultimately improving quality of life for individuals with mobility impairments. His work represents a meaningful step toward seamless human-robot collaboration in clinical and daily settings.
Research Focus
Key Achievements
Top Papers
- 1