Ku Nurhanim
Papers
4
Total Citations
40
H-Index
4
About
Ku Nurhanim is a leading researcher at the intersection of biomedical signal processing and robotic rehabilitation, whose work is fundamentally reshaping how assistive devices interact with human physiology. Her primary research focuses on leveraging surface electromyography (sEMG) signals to create intuitive, patient-responsive control systems for exoskeletons and prosthetic hands. Her most significant contribution lies in pioneering the use of Swarm Intelligence algorithms—specifically Particle Swarm Optimization (PSO)—to develop accurate joint torque estimation models from sEMG data. This breakthrough addresses a critical barrier in stroke rehabilitation: replacing rigid, pre-programmed robotic functions with adaptive systems that respond to a patient’s own muscle activity. Her landmark 2014 paper on this topic has garnered 17 citations, establishing a foundational methodology for the field. In related work (12 citations), she demonstrated the efficacy of a PSO-PID controller for multi-fingered robot hands, showcasing her ability to translate biological signals into precise mechanical control. Through her development of sEMG-based exoskeleton control systems, Ku Nurhanim is paving the way for more natural, effective rehabilitation technologies that empower patients rather than constrain them.
Research Focus
Key Achievements
Top Papers
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