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Activity Number: 515 - Statistical Modeling for Sports Science and Applications
Type: Contributed
Date/Time: Wednesday, August 2, 2017 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Sports
Abstract #324812 View Presentation
Title: Identifying Individuals with Anterior Cruciate Ligament Injury Through Spectral and Linear Discriminant Analyzes
Author(s): Kristin Morgan* and Carolyn Bradshaw Morgan and Heather Bush and Brian Noehren
Companies: University of Connecticut and MECK Limited,LLC and University of Kentucky and University of Kentucky
Keywords: Time Series ; Spectral Analysis ; Regression ; Linear Discriminant Analysis ; Anterior Cruciate Ligament Reconstruction

In human movement biomechanics, healthy and injured populations are often differentiated based on discrete measures, such as peak knee flexion angles. Discrete measures do not capture the changing dynamics that can characterize altered joint motion in injured populations. To capture these changing dynamics, spectral and damping analyses will be employed. The results of these analyses will be used to develop a regression model to classify individuals into healthy and injured populations.

This study will use fast Fourier transform and control theory techniques to obtain frequency, phase, damping and stability metrics to characterize gait in healthy individuals and post anterior cruciate ligament (ACL) reconstruction individuals. Then linear discriminant analysis will be used to identify the critical variables that best classify the healthy and injured populations. It will also yield a model that describes the relationship between these variables that are very critical to identifying individuals with adverse gait biomechanics. This analysis will allow us to potentially predict individuals with future injury risk and design more targeted ACL prevention and rehabilitation programs.

Authors who are presenting talks have a * after their name.

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