Abstract
An MBientLab MetaMotionR device was used to collect accelerometer and gyroscope data when worn on the left and right wrist while a subject curled. This data was analyzed, and features were generated. Feature files were annotated with appropriate classes and used as training data in a decision tree algorithm within MATLAB. The feature generation process was iterated, showing improvements in each iteration. The following conclusions were drawn from the outcomes of various models that were created: acceleration data holds more importance than gyroscope data, the variance feature holds more importance during the sweeping motion than other evaluated features, the mean feature holds more importance during the shooting motion than other evaluated features, sensor location can be predicted by the dataset and does not greatly affect the outcome of predicted classes, the side that the subject sweeps on can be predicted and does not greatly affect the outcome of predicted classes, and the direction the subject curls the stone can be predicted by the dataset.