BME 2643 - AI Applications in Biomechanics

This asynchronous course introduces the fundamentals of artificial intelligence and machine learning (AI/ML) as applied to human biomechanics. BME 2643 is designed as a follow-up to the introductory biomechanics course BME 2641.  This course focuses on how data-driven methods can be used to analyze movement, classify biomechanical patterns, and support decision making in sports, rehabilitation, and human performance. Students learn foundational concepts in data processing, feature extraction from kinematic and kinetic signals, and basic AI/ML model workflows using simplified datasets. All content is delivered through short online modules supported by guided examples and MATLAB exercises. Prior exposure to basic biomechanics concepts and basic MATLAB knowledge is expected, but no advanced programming skills are required.
Prereq: BME 2641  or BME 3410  
Note: None
This course meets the following Raider Core CLO Requirement: None

• Describe the basic principles of AI and machine learning and explain their relevance to human biomechanics
• Identify common biomechanical data types (e.g., motion capture variables, force data, EMG) and understand how they are prepared for ML analysis
• Extract simple biomechanical features, such as joint angle patterns and temporal parameters, from kinematic or kinetic datasets
• Apply basic ML workflows (e.g., training/testing split, model evaluation, interpretation of accuracy metrics) to introductory biomechanics examples
• Use basic tools (minimal or no coding required) to build and test a simple model, such as movement classification or activity recognition
• Interpret AI/ML output in a biomechanics context and articulate the practical significance and limitations of model results
• Recognize ethical considerations in human movement data collection and AI-assisted decision making, including privacy, bias, and appropriate use of predictive tools

• Anthropometric measurements
• Kinematic and kinetic analysis of a major human joint
• Analysis of Impulse forces while walking/jumping
• EMG signal acquisition and processing
• Center of mass/pressure motion during different stands
• Analysis of the gait function