Mehrdad Davoudi

Supported gait and motion assessments in the orthopedic gait lab, ensuring high-quality data to improve clinical evaluations. Specialized in processing EMG and gait data, leveraging AI and machine learning to advance clinical decision-making and patient outcomes. Communicated research at seminars and conferences, distilling complex data into actionable insights. Contributed to a successful DFG-funded EMG processing project, showcasing my ability to manage research and drive innovation.

Conducted comprehensive gait and motion assessments, processing data to extract valuable insights that supported clinical decision-making and research initiatives. Collaborated with multidisciplinary teams to analyze patient data, identify movement abnormalities, and contribute to the development of personalized treatment plans, ultimately enhancing patient care and outcomes.

Researching muscle activation patterns in cerebral palsy, using AI, signal processing, and EMG analysis to enhance gait assessment and clinical decision-making. Integrating statistical modeling and machine learning, I contribute to advancements in biomechanics and surgical applications. With expertise in programming and computational modeling, I develop algorithms for data analysis, optimizing biomechanical assessments and driving clinical innovations.

Conducted musculoskeletal modeling and simulation at Sharif University of Technology, Iran’s leading engineering institution, to analyze the biomechanical effects of a rehabilitation device on shoulder muscle activation. Applied advanced computational biomechanics and data-driven modeling to assess time-varying muscle synergies, providing valuable insights into movement efficiency and rehabilitation strategies.

Studied the preliminary design and theory behind internal combustion engines, focusing on combustion processes and performance principles. Developed a strong understanding of engine systems and engineering problem-solving.