Zaid Bassfar
Papers
4
Total Citations
73
H-Index
4
About
Zaid Bassfar is a leading researcher at the intersection of assistive robotics, IoT-enabled healthcare, and fuzzy graph theory. His most impactful work, the 2022 study on a finger-gesture controlled wheelchair with integrated IoT fall detection (33 citations), directly addresses the critical gap between advanced robotic wheelchair technologies and their real-world accessibility for millions of disabled individuals. Bassfar’s contributions extend to social robotics, where his 2023 paper on SLAM-based localization and navigation for the Pepper robot (31 citations) provides a robust framework for autonomous indoor navigation, overcoming key challenges in obstacle avoidance and path optimization. In parallel, he advances theoretical computer science by applying m-polar fuzzy environments to solve complex robotics manufacturing allocation problems, and by developing local fractional strong metric dimensions for rotationally symmetric planar networks—tools that enhance sensor networking and robot navigation algorithms. Through this dual focus on practical assistive systems and foundational graph-theoretic methods, Bassfar’s work has garnered significant attention, with his most cited papers accumulating over 70 citations, positioning him as a key innovator in making intelligent robotic systems both safer and more accessible.
Research Focus
Key Achievements
Top Papers
- 1Finger-Gesture Controlled Wheelchair with Enabling IoT33 citations · 2022
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