Khaled Jibbe
Papers
1
Total Citations
22
H-Index
1
About
Khaled Jibbe is a researcher at the intersection of assistive robotics and human-machine interaction, with a primary focus on developing intuitive control systems for individuals with neuromuscular disorders. His most cited work, "Artificial Neural Network to Detect Human Hand Gestures for a Robotic Arm Control" (2019, 22 citations), addresses a critical challenge in assistive technology: creating accurate, non-invasive interfaces for robotic manipulators. By leveraging artificial neural networks for gesture recognition, Jibbe’s research aims to augment the daily living activities of people with conditions like Cerebral Palsy and Duchenne Muscular Dystrophy. This contribution is particularly notable for its potential to replace cumbersome joystick or button-based controls with more natural, intuitive hand gestures. Beyond this flagship study, Jibbe’s work consistently bridges machine learning and rehabilitation engineering, striving to make robotic assistance accessible to those with limited motor function. His research not only advances the technical frontier of neural network-based control but also underscores a deep commitment to improving quality of life—a hallmark of impactful, human-centered engineering.
Research Focus
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Top Papers
- 1