School of Medicine and Health Sciences Poster Presentations

Title

Depth-Camera Measured Biomechanics of the Lower Extremity Reveal Movement Abnormalities and Targets for Prevention in ACL Reconstructed Patients

Document Type

Poster

Abstract Category

Prevention and Community Health

Keywords

Prevention, Motion analysis, ACL injury, Biomechanics

Publication Date

Spring 5-1-2019

Abstract

This study proposes a new way to promote and improve ACL injury-prevention programs through a low-cost, clinically-accessible depth camera called the Microsoft Kinect 2. The depth camera's ability to accurately quantify knee kinematics in real-time makes it a unique, non-invasive, and quick means of identifying patterns of motion that predispose to ACL injuries. Custom MATLAB software is used to generate numerical values from each patient's kinematic profile. Our preliminary data shows that Kinect-generated values of hip internal rotation, adduction, and knee flexion can be used to analyze ACL injury and re-injury, simply by asking patients to perform three single leg squats. The analysis highlights motions, such as increased internal rotation, that are common in increasing the probability of re-injury. This is an innovative approach to promoting prevention, improving patient quality of life, and reducing healthcare burden/costs. By demonstrating the efficacy of low-cost, user-friendly technology, this study hopes to increase momentum for technology's role in ACL-prevention programs while inspiring patients to take accountability for their health.

Open Access

1

Comments

Presented at Research Days 2019.

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Depth-Camera Measured Biomechanics of the Lower Extremity Reveal Movement Abnormalities and Targets for Prevention in ACL Reconstructed Patients

This study proposes a new way to promote and improve ACL injury-prevention programs through a low-cost, clinically-accessible depth camera called the Microsoft Kinect 2. The depth camera's ability to accurately quantify knee kinematics in real-time makes it a unique, non-invasive, and quick means of identifying patterns of motion that predispose to ACL injuries. Custom MATLAB software is used to generate numerical values from each patient's kinematic profile. Our preliminary data shows that Kinect-generated values of hip internal rotation, adduction, and knee flexion can be used to analyze ACL injury and re-injury, simply by asking patients to perform three single leg squats. The analysis highlights motions, such as increased internal rotation, that are common in increasing the probability of re-injury. This is an innovative approach to promoting prevention, improving patient quality of life, and reducing healthcare burden/costs. By demonstrating the efficacy of low-cost, user-friendly technology, this study hopes to increase momentum for technology's role in ACL-prevention programs while inspiring patients to take accountability for their health.