Manal Gafar
Papers
1
Total Citations
21
H-Index
1
About
Manal Gafar is a rising researcher at the intersection of the Internet of Sensing Things (IoST), robotics, and human–machine interaction. Her most-cited work, “IoST-Enabled Robotic Arm Control and Abnormality Prediction Using Minimal Flex Sensors and Gaussian Mixture Models” (2024, 21 citations), introduces a groundbreaking system that fuses IoST with robotics to control a six-degree-of-freedom robotic arm using just four strategically placed flex sensors. This low-cost, intuitive interface is designed to empower individuals with limb differences, offering a novel pathway for assistive technology. By integrating Gaussian Mixture Models, Gafar’s system not only enables precise control but also predicts abnormalities in sensor data, enhancing reliability and safety. This work has quickly garnered attention for its practical impact and innovative sensor fusion approach. Gafar’s research sits at the nexus of accessibility, sensor technology, and intelligent control, promising to shape the future of inclusive robotics. Her contributions demonstrate a clear commitment to translating cutting-edge sensing and machine learning into real-world solutions for human augmentation and rehabilitation.
Research Focus
Key Achievements
Top Papers
- 1