Behnam Behinaein
Papers
1
Total Citations
14
H-Index
1
About
Behnam Behinaein is a researcher whose work lies at the intersection of biomedical signal processing, machine learning, and human–machine interaction. His primary research focuses on the development of robust computational models for estimating muscle force from electromyography (EMG) signals—a critical challenge in prosthetics, rehabilitation, and assistive robotics. In his most-cited work, "Bagged tree ensemble modelling with feature selection for isometric EMG-based force estimation" (2022, 14 citations), Behinaein introduced a novel approach combining bagged tree ensembles with systematic feature selection to improve the accuracy and interpretability of force predictions from surface EMG. This contribution addresses the inherent variability and noise in biological signals, offering a practical solution for real-time applications. His work has been recognized for bridging the gap between advanced ensemble learning techniques and real-world clinical needs. With a growing citation footprint, Behinaein’s research continues to influence the design of intelligent, adaptive interfaces that can decode human intent from muscle activity, making him a promising voice in the field of biosignal-based control systems.
Research Focus
Key Achievements
Top Papers
- 1