I'm Andrew and I am a student at Purdue University. My interests are in Artifical Intelligence and Computer Science in general, which led me to do research involving these topics. My favorite subjects are STEM subjects, so I enjoy math, physics, and other similar classes most.

My research addresses the challenge of analyzing the form of a human run without the use of sensors attached to the athlete's body by proposing a new analysis method. The method involves using computer vision to collect data about an athlete's run and analyzing it using a machine learning model that is based on a professional runner. The goal of the machine learning model is to correct the form of an athlete in an inputted video by making predictions about their motion. Through analyzing the motion of an Olympic runner with the Spearman Rank correlation test, the research concludes that modeling the run with machine learning is feasible. The creation of said machine learning algorithm is judged successful with limited accuracy. The potential application of the model in the real world is judged based on Paul Fleming's findings in “Athlete and coach perceptions of technology needs for evaluating running performance” (2018).