Papers
4
Total Citations
62
H-Index
3
About
Khalil Ullah’s research bridges the gap between human physiology and intelligent robotic systems, with a focus on biomedical signal processing, human-machine interfaces, and remote healthcare technologies. His most influential work centers on modeling the relationship between electromyographic (EMG) signals and joint torque—a critical challenge in prosthetics and rehabilitation robotics. In his highly cited 2009 paper, he developed a nonlinear regression model to map EMG signals to elbow joint torque, advancing beyond conventional filtering methods to improve accuracy. This foundational work has garnered 32 citations and continues to inform biomechanical modeling. Ullah also made notable contributions to assistive technology for individuals with severe motor impairments, designing a low-cost, single-channel EEG-based communication system for people with locked-in syndrome (18 citations). His 2011 study systematically examined factors affecting EMG-to-torque models, further refining signal processing techniques. More recently, he has explored the integration of digital twins and the Internet of Robotic Things for autonomous remote patient monitoring, publishing in 2024. By combining rigorous mathematical modeling with practical, low-cost solutions, Ullah’s work directly impacts rehabilitation engineering, brain-computer interfaces, and the future of decentralized healthcare.
Research Focus
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