Available at: https://digitalcommons.calpoly.edu/theses/2866
Date of Award
6-2023
Degree Name
MS in Computer Science
Department/Program
Computer Science
College
College of Engineering
Advisor
Sumona Mukhopadhyay
Advisor Department
Computer Science
Advisor College
College of Engineering
Abstract
Cognitive motor integration (CMI), the simultaneous coordination between cerebral function and motor output, is known to deteriorate following a mild traumatic brain injury (mTBI). This thesis explores the relationship between mTBI, CMI, and the performance of elite athletes in the National Hockey League (NHL). The approach focuses on examining the predictive value of various supervised Machine Learning (ML) models with an emphasis on Explainable Artificial Intelligence (XAI) models. Since the ML solution is intended to complement human scouting decisions, we evaluate the experiments based on both interpretability and accuracy on a limited class imbalanced dataset. The contributions of this research are two-fold based on the following research problems: Firstly, the problem of scouting decisions for amateur hockey players to play in the field is addressed by exploring a set of test scores from a neuroscience experiment involving visuomotor performance metrics. Formulated as a supervised binary classification task, results demonstrate that the trained XAI trained models effectively capture the relationship that determines whether amateur hockey players with a history of concussions are likely to play in the NHL. Specifically, we find the best-performing model to be Weighted-Decision Tree trained using all features proposed in this study. Secondly, the effect of previous concussions on scouting decisions is examined by visuomotor metrics and indicators of NHL performance using XAI models. This problem is also formulated as a supervised binary classification task and results show that the trained XAI models are able to predict concussion history using the visuomotor metrics. While results for this question are inconclusive, we give evidence from current neuroscience literature to support why these models do not reach satisfactory performance. Unlike previous research that mainly relies on physical metrics, our work is novel as it utilizes data derived from a neuroscience test, capturing persistent neurocognitive deficits in elite hockey athletes following concussions.
Included in
Business Analytics Commons, Neurosciences Commons, Other Computer Engineering Commons, Physiological Processes Commons, Sports Management Commons