Analysis of Runner Biomechanics Using Edge Detection and Image Processing Techniques to Determine Pronation Levels Asa Kusuma

The Problem: Determining Pronation Pronation: Ankle rolling inward on impact Over-pronation major cause of running injuries Hard to determine with the eye, subtle

Pronation and Runners Every runner should pronate a moderate amount Too much pronation  Strains achilies tendon  Puts pressure on Tibia  Strains inner knee Not enough pronation  Supination  Puts pressure on Fibula  Strains IT Band OverpronationSupination

The Setup 2 nd Quarter

How the Setup Works Camera  Takes 20 Frames per second  Produces 2 relevant images Before impact At impact Light  Ensures universal lighting Horizontal Tape  Keeps runner in the same position relative to the camera White Background  Helps with edge detection

Method: Edge Shift Get 2 Images, before and after impact  Edge Detect  Isolate the inner edge of foot/leg  Find average x coordinate of edge Equalize image edges Compare x coordinates of edges

Eliminating Noise Upper portion of edge detection usually accurate Run normal edge detection, store edge pixels From top of edge, work down Only include edge pixels which have similar x- coordinates Allow for more variance in x-coordinate for larger differences in y-coordinate

Standardize images  Standard  Not standard

Implementation of a GUI Graphical User Interface library EasyGUI Allows event based GUI elements Runs within the python script Still must start program in terminal

Select Images

Select Which Leg

Select Pronation

Display Results

What Edges Look Like NeutralSupinator Over-Pronator

Determining Relative Factors Camera  What’s the resolution of the camera?  What’s the FPS of the image capture  After testing, doesn’t matter Shift Method  How much shifting constitutes over-pronation?  Testing on multiple subjects 1-5 pixel shift, supinator 6-10 pixel shift, neutral 11+ pixel shift, over-pronator