Mouloud Tair

University of Algiers Benyoucef Benkhedda

Papers

Total Citations

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About

Mouloud Tair is a researcher whose work sits at the intersection of mechatronics, rehabilitation robotics, and biomedical signal processing. His primary contributions focus on developing intelligent assistive devices for human motor recovery and clinical diagnosis. Tair’s most cited work, “Artificial neural networks based myoelectric control system for automatic assistance in hand rehabilitation” (2017, 7 citations), pioneers the use of EMG signals to drive computer-controlled systems for tendon gliding exercises, a cornerstone of hand therapy. He has also made significant strides in neuro-vestibular assessment with his 2023 paper on an optimal 3-PRS parallel robotic platform for vertigo diagnosis and balance rehabilitation, offering a cost-effective tool for dynamic posturography. Additionally, his 2016 design of a 5-DOF upper limb active exoskeleton demonstrates expertise in combining mechanical design with preliminary control strategies for shoulder and elbow rehabilitation. With a portfolio that bridges artificial neural networks, robotic mechatronics, and clinical application, Tair’s work is steadily gaining recognition for its potential to transform patient care through automation and precision engineering.