An Interactive Model to Teach Motor Skills

There has been a tremendous increase in our knowledge of hum motor performance over the last few decades. Our theoretical understanding of how an individual learns to move is sophisticated and complex. It is difficult however to relate much of this information in practical terms to physical educators, coaches, and therapists concerned with the learning of motor skills (Shumway-Cook & Woolcott, 1995). Much of our knowledge stems from lab testing which often appears to bear little relation to real-life situations. This lack of ecological validity has slowed the flow of information from the theorists and researchers to the practitioners. This paper is concerned with taking some small aspects of motor learning theory, unifying them, and presenting them in a usable fashion. The intention is not to present a recipe for teaching motor skills, but to present a framework from which solutions can be found. If motor performance research has taught us anything, it is that every individual and situation presents unique challenges. By increasing our ability to conceptualize the learning situation we should be able to develop more flexible and adaptive responses to the challege of teaching motor skills. The model presented here allows a teacher, coach, or therapist to use readily available observations and known characteristics about a motor task and to conceptualize them in a manner which allows them to make appropriate teaching/learning decisions.

Computational Methods for Identifying Hypertrophic Cardiomyopathy using 12-lead ECG

Hypertrophic cardiomyopathy (HCM) is a cardiovascular disease where the heart muscle is partially thickened and blood flow is - potentially fatally - obstructed. It is one of the leading causes of sudden cardiac death in young people. Electrocardiography (ECG) and Echocardiography (Echo) are the standard tests for identifying HCM and other cardiac abnormalities. The American Heart Association has recommended using a pre-participation questionnaire for young athletes instead of ECG or Echo tests due to considerations of cost and time involved in interpreting the results of these tests by an expert cardiologist. Initially we set out to develop a classifier for automated prediction of young athletes’ heart conditions based on the answers to the questionnaire. Classification results and further in-depth analysis using computational and statistical methods indicated significant shortcomings of the questionnaire in predicting cardiac abnormalities. Automated methods for analyzing ECG signals can help reduce cost and save time in the pre-participation screening process by detecting HCM and other cardiac abnormalities. Therefore, the main goal of this dissertation work is to identify HCM through computational analysis of 12-lead ECG. ECG signals recorded on one or two leads have been analyzed in the past for classifying individual heartbeats into different types of arrhythmia as annotated primarily in the MIT-BIH database. In contrast, we classify complete sequences of 12-lead ECGs to assign patients into two groups: HCM vs. non-HCM. The challenges and issues we address include missing ECG waves in one or more leads and the dimensionality of a large feature-set. We address these by proposing imputation and feature-selection methods. We develop heartbeat-classifiers by employing Random Forests and Support Vector Machines, and propose a method to classify full 12-lead ECGs based on the proportion of heartbeats classified as HCM. The results from our experiments show that the classifiers developed using our methods perform well in identifying HCM. Thus the two contributions of this thesis are the utilization of computational and statistical methods for discovering shortcomings in a current screening procedure and the development of methods to identify HCM through computational analysis of 12-lead ECG signals.