Biomechanics Data in Python & AI

About this course

This hands-on course bridges biomechanics and coding, built on the concepts from A Hands-On Guide to Biomechanics Data Analysis with Python and AI. You’ll learn how to process, analyze, and visualize human movement data using Python, Google Colab, and AI tools—no prior programming required. Step by step, we move from raw motion capture, force, and EMG signals to clear insights about posture, performance, and efficiency.Through guided notebooks and real datasets, you’ll explore:Data cleaning, filtering, and event detection in biomechanicsForce-plate and motion data analysis with NumPy and Pandas2D/3D visualization and report generation in ColabBasic machine learning for movement classification and predictionYou’ll also gain practical skills for parsing C3D files, aligning markers and forces, normalizing units, detecting gait events, and computing key metrics such as stride time, GRF peaks, and symmetry indices. Each module follows the same reproducible pipeline used by biomechanics labs worldwide—Input → Parse → Analyze → Visualize → Report.By the end, you’ll be able to transform complex biomechanical data into meaningful, shareable results—ready for research, clinical work, sports analysis, or AI modeling. Includes Colab notebooks, sample datasets, code templates, and report builders so you can apply everything immediately to your own projects.Who is it for? Students, clinicians, coaches, and researchers seeking a practical, modern toolkit. You’ll complete bite-size projects (e.g., compare shoes or techniques) and a capstone that imports C3D/CSV, computes key features, visualizes cycles, and exports an HTML/CSV mini-report. Clear checklists, guardrails, and starter code keep you moving—from first plot to publishable, reproducible results.