Papers
8
Total Citations
90
H-Index
4
About
Nasir Rashid’s research lies at the intersection of robotics, computer vision, and brain-computer interfaces, with a focus on creating intelligent systems that perceive, navigate, and assist. His most impactful work, “Human activity recognition using 2D skeleton data and supervised machine learning” (41 citations), advances vision-based HAR for applications like video surveillance and ambient intelligence, using only 2D data to make recognition more accessible. In robotics, Rashid has made significant contributions to unmanned ground vehicles (UGVs), including an FPGA-based control system for a UGV with a 5-DOF robotic arm (16 citations) and a throwable, impact-resistant UGV designed to survive falls from 7 meters. He has also developed a semi-autonomous stair-climbing platform for rough terrains, enhancing search-and-rescue capabilities. Extending into assistive technology, Rashid proposed a novel framework for classifying motor imagery EEG signals using logistic regression (16 citations), enabling brain-computer interfaces for people with motor disabilities. His work on inverse kinematics for redundant manipulators and image-based visual servoing for object tracking further demonstrates his breadth in robotic control. With over 90 total citations, Rashid’s research is shaping practical, deployable systems that bridge perception, mobility, and human assistance.
Research Focus
Key Achievements
Top Papers
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- 5Design and Analysis of Throwable Unmanned Ground Vehicle4 citations · 2023
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