Analyzing Human Movements - Introducing A Framework To Extract And Evaluate Biomechanical Data

Abstract

This study discusses possible sources of discrepancy between findings of previous human motion studies and presents a framework that seeks to address these issues. Motion analysis systems are widely employed to identify movement deficiencies - e.g. patterns that potentially increase the risk of injury or inhibit performance. However, findings across studies are often conflicting in respect to what a movement deficiency is or the magnitude of the relationship to a specific injury. To test the information content of movement data, a framework was build to differentiate between movements performed by a control (NORM) and abnormal (IMP-L and IMP-C) cohort using solely movement data. Movement data was recorded during jumping, hopping and change of direction exercises and was mathematically decomposed into subject scores. Subjects scores were then used to identify the most appropriate machine learning technique, which was subsequently utilized to create a prediction model that classified if a movement was performed by: IMP-L, IMP-C or NORM. The Monte Carlo method was used to obtain a measure of expected accuracy for each step within the analysis. Findings demonstrate that even the worst classification model outperformed the best guess observed and that not all members of the NORM group represent a NORM pattern as they were repeatedly classified as IMP-L or IMP-C. This highlights that some NORM limbs share movement characteristics with the abnormal group and consequently should not be considered when describing NORM.