Papers

10

Total Citations

231

H-Index

6

About

Farid Mobasser’s research bridges human physiology and robotics, focusing on human-machine interaction, haptics, and assistive technologies. His most influential work centers on estimating human hand and arm dynamics using electromyography (EMG) signals—a critical capability for controlling prosthetic limbs, enabling intuitive human-robot collaboration, and advancing haptic feedback systems. In his highly cited 2007 paper (69 citations), Mobasser introduced a method using Fast Orthogonal Search to estimate wrist force from EMG signals, offering a low-cost, portable solution for real-time force monitoring. His 2006 work on neural-network-based contact force observers (68 citations) further advanced transparent teleoperation and haptic control, allowing robots to sense and respond to human-applied forces without bulky sensors. Mobasser also developed a novel online estimation technique for human arm dynamics using a second-order quasi-linear model and Moving Window Least Squares (40 citations), enabling adaptive human-machine interfaces. Beyond these contributions, he led the design of ARVAND, a middle-sized soccer robot for RoboCup competitions, demonstrating his versatility in both theoretical modeling and practical robotic systems. With over 230 cumulative citations, Mobasser’s work continues to inform research in rehabilitation robotics, haptics, and human performance analysis.

Research Focus

Key Achievements

6
H-Index
10
Papers
231
Total Citations
23
Avg Citations/Paper
🏆 Most Cited Paper
Estimation of Elbow-Induced Wrist Force With EMG Signals Using Fast Orthogonal Search
69 citations · 2007
📈 Most Prolific Year: 2006 (4 Papers)
🤝 Key Collaborators: 17
🏛 Institutions: Invixium (Canada), Queen's University, Sharif University of Technology

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
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