Papers

17

Total Citations

193

H-Index

8

About

Anwar P. P. Abdul Majeed is a leading researcher at the intersection of robotics, rehabilitation engineering, and artificial intelligence. His work focuses on developing intelligent robotic systems for human assistance, particularly through the classification of human motion intention using machine learning models applied to electromyography (EMG) signals—a key enabler for seamless human-robot collaboration. His most cited paper (57 citations) demonstrates how time-domain EMG features can predict movement intent, advancing the design of responsive exoskeletons and prosthetics. Majeed has made significant contributions to lower-limb exoskeletons for gait rehabilitation, including a hybrid active force control system (23 citations) and a clinically considered lower extremity exoskeleton (17 citations). He has also explored upper-limb assistive strategies and autonomous agricultural robotics, such as a deep learning-based tomato harvesting system (13 citations). With over 170 citations across his top papers, Majeed’s work spans from dynamic ankle-foot orthoses to quadrotor control and sensor fusion for mobile robots. His research is notable for bridging theoretical machine learning with practical, human-centered robotic applications, making him a key figure in the development of assistive and rehabilitation technologies.

Research Focus

Key Achievements

8
H-Index
17
Papers
193
Total Citations
11
Avg Citations/Paper
🏆 Most Cited Paper
The classification of movement intention through machine learning models: the identification of significant time-domain EMG features
57 citations · 2021
📈 Most Prolific Year: 2021 (4 Papers)
🤝 Key Collaborators: 51
🏛 Institutions: Universiti Malaysia Pahang Al-Sultan Abdullah, Sunway University, Xi’an Jiaotong-Liverpool University

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago