Can Commonly used exercises give insight into rehabilitation status: Introducing a framework that seeks to judge return to play after ACL injury - Richter, C.; King, E.; Strike, S.; Franklyn-Miller, A. (Sportinformatik XII. 12. Symposium der dvs-Sektion "Sportinformatik und Sporttechnologie" vom 5.-7. September 2018 in Garching. Abstracts., 2018)
Motion analysis systems are widely employed within universities and clinical facilities to identify movement pattern that hamper performance, increase the risk of injury or describe anomalies. Knowing such pattern allows a targeted manipulation of a movement and can improve the outcome of a task in respect to performance, risk of injury and anomaly. However, there is yet little scientific evidence in respect to what a 'normal' movement is or what exercises can highlight movement deficiencies best - e.g. findings across studies are often conflicting (Hewett et al., 2005 & Krosshaug et al., 2016). However, conclusions are commonly based on an analysis that has not changed much over the last decades. To date, most studies still use a discrete point feature extraction examining best or average trail using a magnitude based inference testing. This might explain why studies are so often conflicting. The aim of this paper is to introduce a framework that mathematical reduces movement data in combination with a machine learning technique to examine the ability of a battery of exercises to objectively detect describe anomalies.