Juan Ni
Papers
1
Total Citations
15
H-Index
1
About
Juan Ni has made significant contributions to the field of biomedical signal processing and assistive rehabilitation technology. Her primary research focuses on electromyography (EMG) signal analysis and machine learning for hand motion pattern recognition, with the goal of improving control systems for prosthetic and assistive medical devices. In her highly cited 2020 study, she systematically compared multiple artificial intelligence algorithms to optimize both operational accuracy and response time in EMG-based control systems—a critical challenge for clinical rehabilitation applications. This work, which has garnered 15 citations, demonstrates her commitment to bridging the gap between raw physiological signals and practical, real-time assistive technologies. By addressing the trade-off between precision and latency, Ni's research directly impacts the development of more responsive and intuitive prosthetic devices, enhancing quality of life for individuals with motor impairments. Her work stands as a valuable resource for researchers and engineers working at the intersection of signal processing, machine learning, and human-machine interfaces.
Research Focus
Key Achievements
Top Papers
- 1