Jinyi Long
Papers
2
Total Citations
19
H-Index
2
About
Jinyi Long is a leading researcher in rehabilitation robotics and brain–computer interfaces (BCIs), with a focus on advancing lower-limb assistive technologies. Their work bridges multi-modal sensor fusion and neural signal decoding to enhance human–machine interaction. Long’s most cited paper, "Transferable multi-modal fusion in knee angles and gait phases for their continuous prediction" (2023, 15 citations), introduces a novel framework that integrates kinematic and physiological data to accurately predict gait phases and joint angles—critical for controlling exoskeletons and prosthetics. This approach improves real-time adaptability and transferability across subjects, addressing a key challenge in rehabilitation robotics. Another notable contribution, "A novel noninvasive brain–computer interface by imagining isometric force levels" (2022, 4 citations), explores how imagined force levels can be decoded from EEG signals, expanding BCI applications for motor recovery. Long’s work has significant implications for personalized rehabilitation, enabling more intuitive and responsive assistive devices. Their research is widely cited in robotics and neural engineering communities, reflecting its impact on developing smarter, user-adaptive systems for individuals with motor impairments.
Research Focus
Key Achievements
Top Papers
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- 2