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

2nd 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