Speaker
Description
Measuring and understanding human motion is crucial in several domains, ranging from neuroscience, to rehabilitation and sports biomechanics. The study of human motion is commonly done through marker-based techniques and motion capture systems. If on one hand these methods are precise and reliable, on the other they present some disadvantages, in particular they are expensive, encumbering, and time consuming. For these reasons recently the research of cheaper and easier markerless techniques had made great strides. In particular, we are witnessing a steady growth in the design and implementation of computer vision and machine learning algorithms applied to plain video recordings of human movements. This type of analysis facilitates the extraction of features that give qualitative and quantitative information about human motion and that can be used for detecting, characterizing, and understanding motor behavior and deficits associated with neurological diseases. Our research team at the University of Genoa has combined multiple expertises to design and implement a markerless pipeline based on state of the art algorithms and apply it to different case studies. In this talk we will show some preliminary results.
