Postgraduate Certificate in Biomechanical Analysis via AI

• Unit 1: Introduction to Biomechanical Analysis – Understanding the fundamental concepts of biomechanics and the role of AI in biomechanical analysis.
• Unit 2: AI Technologies for Biomechanical Analysis – Exploring various AI tools and techniques, including machine learning and deep learning algorithms, for biomechanical analysis.
• Unit 3: Data Acquisition and Preprocessing – Learning about data collection methods, preprocessing techniques, and data cleaning for biomechanical analysis.
• Unit 4: Motion Analysis – Studying the principles of motion analysis, including kinematics and kinetics, and their application in AI-based biomechanical analysis.
• Unit 5: Force and Torque Analysis – Understanding the concepts of force and torque analysis and their implementation in AI-based biomechanical systems.
• Unit 6: Machine Learning Models for Biomechanical Analysis – Diving into the application of machine learning models, including decision trees, random forests, and support vector machines, for biomechanical analysis.
• Unit 7: Deep Learning Models for Biomechanical Analysis – Examining the use of deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), for biomechanical analysis.
• Unit 8: Evaluation and Validation of AI-based Biomechanical Systems – Learning about the evaluation and validation of AI-based biomechanical systems, including performance metrics and statistical analysis.
• Unit 9: Ethical and Legal Considerations in AI-based Biomechanical Analysis – Exploring ethical and legal issues related to AI-based biomechanical analysis and their impact on society.
• Unit 10: Case Studies in AI-based Biomechanical Analysis – Examining real-world case studies that demonstrate the application of AI in biomechanical analysis.