Papers
16
Total Citations
124
H-Index
7
About
Muhammad Faizan Shah is a leading researcher in rehabilitation robotics and industrial automation, with a focus on mechanism design, control strategies, and human-robot interaction. His major contributions span the development of robotic systems for upper limb rehabilitation—particularly for the shoulder and ankle joints—where he has advanced both parallel and serial manipulator designs. Shah’s work on the Virtual Biomechanical Shoulder Robot Model (VBSRM) and deep learning-based inverse kinematics solutions has significantly improved the precision and adaptability of rehabilitation robots. His highly cited review on shoulder rehabilitation robots (30 citations) and his pioneering hybrid impedance control for redundant manipulators (14 citations) underscore his impact, with total citations exceeding 110. Notable achievements include the design of a five-degree-of-freedom mobile welding manipulator for spherical objects and intelligent hybrid control for mobile robots in cluttered environments. Shah’s research integrates biomechanics, control theory, and artificial intelligence, offering practical solutions for neurological rehabilitation and industrial automation. His recent deep learning frameworks for joint angle estimation further highlight his commitment to merging robotics with cutting-edge AI, making his work essential for students and researchers advancing assistive and autonomous robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Mechanism Design and Control of Shoulder Rehabilitation Robots: A Review30 citations · 2023
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- 3Inverse Jacobian based hybrid impedance control of redundant manipulators14 citations · 2006
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