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Machine learning as a tool to understand and predict ankle sprain
Szigeti-Larenne, Jordan Mazur, Mykola Petrella, Anthony
Szigeti-Larenne, Jordan
Mazur, Mykola
Petrella, Anthony
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2024-04
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Abstract
Ankle injury is a fundamental and recurring issue in high impact activity that affects athletes, military personnel, and everyday people. Current attempts at ankle injury mitigation look towards passive devices that simply support the ankle through bound structure built through the device. The goal of this study was to identify the risk factors and indicators that are actively associated with ankle sprain, implement them into a machine learning algorithm, and establish a program that could significantly predict ankle sprain. The currently understood risk factors were distinguished through previous research papers and were used as key features for the machine learning algorithm. Based on these features machine learning algorithms were used to predict ankle sprain. This preliminary research sets the stage for future research into wearable devices that could be implemented into a passive ankle brace and predict sprain while in motion. This prediction could then be implemented into active devices that will increase the stiffness of the
brace only when sprain is likely.
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