Self-Organized Maps for the Analysis of the Biomechanical Response of the Knee Joint During Squat-Like Movements in Subjects Without Physical Conditioning

Biomechanical analyses provide an extensive source of data that are deeply explored by physicians, engineers and trainers from the mechanical and physiological point of view. This data includes kinetic and kinematic parameters that are quite useful to study human locomotion. However, most of these analyses stay on a very superficial level. Recently data and computational science expanded their coverage to new areas and new analysis tools are available. These analyses include the use of machine learning tools for data mining processes. All of these new tools open a total new level of data analysis, thus newer and deeper questions are proposed in order to provide more accurate prediction results with strict decision support. On the other hand, Squat is an exercise widely used for physical conditioning since it puts into operation various muscles at the same time of the lower and upper train. However bad squatting could drive to injuries at the back and knee level. These injuries are especially common in patients without physical conditioning. In this study, squat data is analyzed using Self-Organizing Maps (SOM) to identify possible relevant parameters from the subjects that could affect the movement performance especially at the knee joint.