Dr. Amin Hekmatmanesh l Algorithm Development | Best Researcher Award
LUT University, Finland
Author Profile
🔎 Summary
Dr. Amin Hekmatmanesh is a skilled biomedical engineer and data scientist, specializing in wearable sensors, AI, and machine learning applications for health technologies. He has extensive experience in biosignal processing (EEG, ECG, PPG, EMG, GSR, IMU), rehabilitation robotics, and medical device development. With a Ph.D. in Mechanical Engineering (Biomedical Engineering), he has made significant contributions to the field by developing real-time systems for rehabilitation and health monitoring. His research focuses on using AI to advance rehabilitation robotics and human-robot interactions, publishing over 30 peer-reviewed articles. He is also an active reviewer and editorial board member for several high-ranking journals.
🎓 Education
Dr. Hekmatmanesh holds a Ph.D. in Mechanical Engineering (Biomedical Engineering) from LUT University in Finland, where he achieved a GPA of 4.33. His dissertation focused on artificial intelligence and machine learning for EEG signal processing in rehabilitation robotics. He also completed his M.Sc. in Biomedical Engineering at Shahed University in Iran, with a perfect GPA of 4.0, and a B.Sc. in Electrical Engineering from Islamic Azad University, graduating with a GPA of 4.1.
💼 Professional Experience
Dr. Hekmatmanesh has a diverse professional background, having worked as a Lead Project Manager at Mevea Company, where he managed health monitoring system projects, incorporating wearable sensor technology. At Flowgait Company, he contributed to mathematical solutions for horse motion analysis. As a Junior Researcher and Project Manager at LUT University, he focused on wearable sensor systems and health monitoring. Additionally, he worked as a Research Assistant at Tehran University, working on diagnostic algorithms and therapeutic systems in health technologies.
📚 Academic Citations
Dr. Hekmatmanesh has published over 30 peer-reviewed papers, including book chapters and review articles, and his work has contributed significantly to the biomedical engineering and AI fields. His research has received wide recognition, and he is regularly invited to participate in scientific conferences and review for high-impact journals, showcasing his influence and contributions to advancing health technologies.
🔧 Technical Skills
Dr. Hekmatmanesh is highly proficient in biosignal processing, including EEG, ECG, PPG, EMG, GSR, and IMU signals. He is skilled in machine learning and AI, specifically for biosignal classification and real-time system development. His technical expertise extends to embedded electronics, sensor design, and circuit board assembly. He is proficient in Python and Matlab for data analysis and has experience using version control tools like GitHub to manage research projects.
🎓 Teaching Experience
Dr. Hekmatmanesh has been actively involved in teaching and mentoring within the biomedical engineering field. He has supervised PhD students and contributed to academic programs by delivering lectures and guiding research projects in health technologies, machine learning, and rehabilitation robotics.
🔬 Research Interests On Algorithm Development
Dr. Hekmatmanesh’s research interests include the development of wearable sensor systems for health monitoring, AI and machine learning techniques for biosignal classification, and the design and control of rehabilitation robotics. He is also focused on human-robot interactions and improving prosthetics control through innovative AI applications in healthcare.
📖 Top Noted Publications
Nanocaged platforms: modification, drug delivery and nanotoxicity. Opening synthetic cages to release the tiger
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- Authors: PS Zangabad, M Karimi, F Mehdizadeh, H Malekzad, A Ghasemi, …
- Journal: Nanoscale
- Year: 2017
Review of the state-of-the-art of brain-controlled vehicles
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- Authors: A Hekmatmanesh, PHJ Nardelli, H Handroos
- Journal: IEEE Access
- Year: 2021
Neurosciences and wireless networks: The potential of brain-type communications and their applications
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- Authors: RC Moioli, PHJ Nardelli, MT Barros, W Saad, A Hekmatmanesh, …
- Journal: IEEE Communications Surveys & Tutorials
- Year: 2021
A combination of CSP-based method with soft margin SVM classifier and generalized RBF kernel for imagery-based brain computer interface applications
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- Authors: A Hekmatmanesh, H Wu, F Jamaloo, M Li, H Handroos
- Journal: Multimedia Tools and Applications
- Year: 2020
EEG control of a bionic hand with imagination based on chaotic approximation of largest Lyapunov exponent: A single trial BCI application study
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- Authors: A Hekmatmanesh, RM Asl, H Wu, H Handroos
- Journal: IEEE Access
- Year: 2019