Dr. Seyyed Ali Zendehbad | Engineering | Editorial Board Member
Islamic Azad University, Mashhad | Iran
Author Profile
Early Academic Pursuits 🎓
Dr. Zendehbad’s academic journey began with a strong foundation in electronic and information technology engineering. He pursued multiple degrees, culminating in a Ph.D. in Biomedical Engineering from the Islamic Azad University of Mashhad. His doctoral research focused on improving upper limb function in stroke patients using biofeedback and muscle synergy analysis—an innovative approach with profound implications for rehabilitation science.
Professional Endeavors 👨🏫
Dr. Zendehbad has an impressive academic career as a professor and head of the Biomedical Engineering department at various prestigious institutions. He has taught specialized courses such as neuromuscular system control, biological system modeling, and biomedical research methodologies. Beyond academia, he has contributed to industry research, including the development of imaging quality enhancements for functional hard endoscopes.
Contributions and Research Focus 🔬
Dr. Zendehbad’s research primarily focuses on:
✅ Electromyogram (EMG) signal classification and analysis
✅ Muscle synergy patterns in stroke rehabilitation
✅ AI-driven biofeedback and assistive technologies
✅ Telehealth solutions and trustworthy AI applications in medical engineering
His work in stroke rehabilitation, particularly in biofeedback mechanisms and AI-driven recovery systems, has set new benchmarks in the field.
Accolades and Recognition 🏅
Dr. Zendehbad’s pioneering work has been recognized with several prestigious awards:
🏆 First Place - 31st Congress of Neurology and Clinical Electrophysiology (2024)
🏆 First Place - Shahid Beheshti University Startup Competition in Telerehabilitation (2021)
🏆 First Place - Mashhad University of Medical Sciences Startup Competition (2020)
These accolades reflect his outstanding contributions to medical engineering and rehabilitation technologies.
Impact and Influence 🌍
Dr. Zendehbad’s research has had a profound impact on both academia and industry. His contributions to AI-driven rehabilitation technologies have paved the way for more effective stroke recovery methods. Additionally, his role in startup competitions has facilitated innovation in telehealth and telerehabilitation, making cutting-edge healthcare solutions more accessible.
Legacy and Future Contributions 🚀
Dr. Zendehbad continues to push the boundaries of biomedical engineering. His ongoing research in AI applications for fatigue detection (FatigueNet project) and telehealth ethics (Trustworthy AI in Telehealth) demonstrates his forward-thinking approach. His legacy will undoubtedly inspire future researchers and innovators in the field of bioelectric engineering and medical technology.
Publications
📄 TraxVBF: A Hybrid Transformer-xLSTM Framework for EMG Signal Processing and Assistive Technology Development in Rehabilitation
- Authors: Seyyed Ali Zendehbad, Athena Sharifi Razavi, Marzieh Allami Sanjani, Zahra Sedaghat, Saleh Lashkari
- Journal: Sensing and Bio-Sensing Research
- Year: 2025
📄 Identifying The Arm Joint Dynamics Using Muscle Synergy Patterns and SVMD-BiGRU Hybrid Mechanism
- Authors: Seyyed Ali Zendehbad, Hamid Reza Kobravi, Mohammad Mahdi Khalilzadeh, Athena Sharifi Razavi, Payam Sasan Nezhad
- Journal: Frontiers in Biomedical Technologies
- Year: 2024
📄 Presenting a New Muscle Synergy Analysis Based Mechanism to Design a Trackable Visual Biofeedback Signal: Applicable to Arm Movement Recovery After Ischemic Stroke
- Authors: Seyyed Ali Zendehbad, Hamid Reza Kobravi, Mohammad Mahdi Khalilzadeh, Athena Sharifi Razavi, Payam Sasan Nezhad
- Journal: IEEE Access
- Year: 2023
📄 A New Visual Biofeedback Protocol Based on Analyzing the Muscle Synergy Patterns to Recover the Upper Limbs Movement in Ischemic Stroke Patients: A Pilot Study
- Authors: Seyyed Ali Zendehbad, Hamid Reza Kobravi, Mohammad Mahdi Khalilzadeh, Athena Sharifi Razavi, Payam Sasan Nezhad
- Journal: The Neuroscience Journal of Shefaye Khatam
- Year: 2023
📄 Investigation and Analysis of Feature Extraction Methods Based on Multi-Objective Genetic Algorithm and Support Vector Machine for Classification of Electromyogram Signals of Arm Muscles
- Authors: Seyyed Ali Zendehbad, Siyamak Haghipour, Hamid Reza Kobravi, Seyyed Amir Zendehbad
- Journal: Journal of New Research in Engineering Sciences
- Year: 2016