analysis of runner biomechanics using edge detection and image processing techniques to determine...
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Analysis of Runner Biomechanics
Using Edge Detection and Image Processing Techniques to Determine Pronation Levels
Asa Kusuma
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The Problem: Determining Pronation Pronation: Ankle rolling inward on impact Over-pronation major cause of running injuries Hard to determine with the eye, subtle
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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
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The Setup
2nd Quarter
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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
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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
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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
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Standardize images
Standard Not standard
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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
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Select Images
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Select Which Leg
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Select Pronation
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Display Results
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What Edges Look Like
NeutralSupinator Over-Pronator
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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