| Περίληψη: | The Anterior Cruciate Ligament rupture is a very common knee injury during sport activities. Landing after a jump is one of the most prominent human body movement scenarios that can lead to such an injury. The landing - related Anterior Cruciate Ligament (ACL) injury risk factors have been in the spotlight of research interest. Over the years, researchers and clinicians acquire knowledge about human movement during daily - life activities by organizing complex in vivo studies using Motion Capture equipment. The recorded motion data are further analyzed to estimate joint angles and forces and provide insight to the functionality and response of internal structures such as soft tissues, ligaments and muscles. Nevertheless, these experiments feature high complexity, costs and technical and most importantly physical challenges. Computational modeling and simulation overcomes all these limitations and allows for studying musculoskeletal systems. Specifically, predictive simulation approaches offer researchers the opportunity to predict and study new biological motions without the demands of acquiring experimental data. In this thesis, we present a pipeline that aims to predict and identify key parameters of interest that are related to ACL injury during single - leg landings. We examined the following conditions of single - leg landing: a) initial landing height, b) hip internal and external rotation, c) lumbar forward - backward leaning, d) lumbar medial - lateral bending, e) lumbar internal - external rotation, f) muscle forces permutations and g) effort goal weight. Identified on related research studies, we evaluated the following risk factors: vertical Ground Reaction Force, knee joint Anterior force, knee joint Abduction moment, and Quadricep / Hamstring force ratio. Our study clearly demonstrated that ACL injury is a rather complicated mechanism with many associated risk factors which are evidently correlated. Nevertheless, our results were mostly in agreement with other research studies regarding the ACL risk factors. Despite the limitations regarding the adopted modeling assumptions, our pipeline clearly showcased promising potential of predictive simulations to evaluate different aspects of complicated phenomena, such as the ACL injury. Therefore, further improvements can lead to the development of a workflow that can be used by physiotherapists and clinicians on adjusting rehabilitation and training plans based on subject - specific characteristics.
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