Sports analytics tools are becoming more frequently used to help athletes enhance their skills and body strength to perform better and prevent injury. ACL tearing is one of the most common and dangerous injuries in basketball history. This injury occurs most frequently in jumping, landing, and pivoting due to the rapid change of direction and/or sudden deceleration in basketball. Recovering from an ACL injury is a brutal process, can take months – even years – to recover, and significantly decrease the player’s performance after recovery. In most glute exercises such as squatting and lunging, it is hypothesized that the more fatigued a person is, the more they droop their shoulders, they apply more pressure to the ground, and the more they internally rotate their knees which increases ACL injury risk. The goal of this project is to find the relationship between fatigue and different angle measurements in the hips, knees, and back as well as the force applied to the ground to minimize the ACL injury risk. 9 different sensors were attached to a test subject while he conducted 3 glute exercises on a force plate – running side squats, running pivot, and high jumps for 10 trials on each leg before and after an hour of vigorous exercise. Simple Linear Regression, Multivariate Clustering, and Time Series tools were used to compare the data before and after fatigue.
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