Seyyed Ali Zendehbad | Engineering | Editorial Board Member

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Dr. Seyyed Ali Zendehbad | Engineering | Editorial Board Member

Islamic Azad University, Mashhad | Iran

Author Profile

Scopus

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

 

Harikumar Rajaguru | Engineering | Best Researcher Award

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Dr. Harikumar Rajaguru | Engineering | Best Researcher Award

Bannari Amman Institute of Technology | India

Author Profile

Scopus

Orcid

Google Scholar

Early Academic Pursuits

Dr. Harikumar Rajaguru began his academic journey in electronics and communication engineering. He completed his Bachelor of Engineering in Electronics and Communication Engineering from Regional Engineering College, Trichy, affiliated with Bharathidasan University, in 1988. He then pursued a Master of Engineering in Applied Electronics at the College of Engineering, Guindy, under Anna University Chennai, graduating in 1990. His passion for biomedical signal processing led him to obtain a Ph.D. in Information and Communication Engineering, with a specialization in Bio Signal Processing, from Thiagarajar College of Engineering, Madurai, under Anna University Chennai in 2009. His Ph.D. thesis focused on the use of soft computing techniques and non-linear models for the performance analysis and classification of epilepsy risk levels from EEG signals.

Professional Endeavors

Dr. Rajaguru's professional career spans over 33 years in academia. He began as a lecturer and senior lecturer at PSNA College of Engineering and Technology, Dindigul, where he served for ten years. He then worked as an Assistant Professor at PSG College of Technology, Coimbatore, for one year. His journey continued at Amrita Institute of Technology, Coimbatore, as a Senior Lecturer and Assistant Professor for over two years. Dr. Rajaguru also spent time as a research scholar at Thiagarajar College of Engineering, Madurai. Since 2006, he has been a professor at Bannari Amman Institute of Technology, Sathyamangalam, where he has contributed significantly to the field of biomedical signal processing and soft computing.

Contributions and Research Focus

Dr. Rajaguru has made substantial contributions to biomedical signal processing, particularly in the classification of epilepsy risk levels from EEG signals. He has been involved in several funded projects, including the development of an ASIC fuzzy processor for diabetic epilepsy risk level classification, wavelet networks for epilepsy risk levels classification from EEG signals, and a non-invasive photo plethysmographic-based glucometer for mass diabetes screening. His research primarily focuses on applying soft computing techniques and developing non-linear models for analyzing and classifying biomedical signals.

Accolades and Recognition

Throughout his career, Dr. Rajaguru has received numerous accolades and recognition for his contributions to engineering and biomedical signal processing. He has published multiple papers in SCI-indexed journals and conferences, showcasing his research findings and innovations. His work has been recognized with patents for various biomedical devices, including an electro gastrogram system for detecting gastric disorders, a sensor for stress measurement using photo plethysmography, and a device for detecting ventricular tachycardia. These patents highlight his innovative approach to solving complex biomedical problems.

Impact and Influence

Dr. Rajaguru's work has significantly impacted biomedical signal processing, particularly in the analysis and classification of epilepsy risk levels and the development of non-invasive diagnostic tools. His research has provided new insights into the use of soft computing and wavelet networks in biomedical applications. His contributions have influenced both academic research and practical applications in the medical field, improving diagnostic techniques and patient outcomes.

Legacy and Future Contributions

Dr. Rajaguru's legacy lies in his dedication to advancing biomedical signal processing through innovative research and teaching. He has guided numerous students in their research projects, fostering the next generation of engineers and researchers. As he continues his work at Bannari Amman Institute of Technology, he is poised to make further contributions to the field, particularly in developing new diagnostic tools and techniques for medical applications. His commitment to research and education ensures that his influence will be felt for years to come, both in academia and the broader field of biomedical engineering.

 

Notable Publications

Wavelet feature extraction and bio-inspired feature selection for the prognosis of lung cancer − A statistical framework analysis 2024

Processing of digital mammogram images using optimized ELM with deep transfer learning for breast cancer diagnosis 2023 (5)

Exploration and Enhancement of Classifiers in the Detection of Lung Cancer from Histopathological Images 2023 (6)

Detection of Diabetes through Microarray Genes with Enhancement of Classifiers Performance 2023 (1)

Evaluation and Exploration of Machine Learning and Convolutional Neural Network Classifiers in Detection of Lung Cancer from Microarray Gene—A Paradigm Shift 2023 (6)