Brandon Kakos
Papers
1
Total Citations
5
H-Index
1
About
Brandon Kakos is a researcher at the forefront of rehabilitative robotics and human-machine interaction. His work centers on the real-time processing of surface electromyography (sEMG) signals to control assistive exoskeletons, with a particular focus on upper-limb rehabilitation. Kakos’s major contribution lies in bridging the gap between machine learning and practical, online control systems. While most studies have used ML for off-line data analysis, his research demonstrates the feasibility of real-time, multiple-channel shoulder EMG processing using artificial neural networks, enabling more responsive and intuitive motion control for exoskeletons. His most-cited paper, “Real-time Multiple-Channel Shoulder EMG Processing for a Rehabilitative Upper-limb Exoskeleton Motion Control Using ANN Machine Learning” (2021), has garnered 5 citations, reflecting its growing influence in the field. This work is notable for its direct application to improving the quality of life for individuals with motor impairments, showcasing Kakos’s commitment to translating advanced computational methods into tangible assistive technologies. His research continues to push the boundaries of what is possible in neural-controlled rehabilitation devices.
Research Focus
Key Achievements
Top Papers
- 1