Correlation Dimension-Based Recognition of Simple Juggling Movements

The last decade of technological development has given raise to a myriad of new sensing devices able to measure in many ways the movements of human arms. Consequently, the number of applications in human health, robotics, virtual reality and gaming, involving the automatic recognition of the arm movements, has notably increased. The aim of this paper is to recognise the arm movements performed by jugglers during their exercises with three and four balls, on the basis of few information on the arm orientation given by Euler Angles, measured with a cheap sensor. The recognition is obtained through a linear Support Vector Machine after a feature extraction phase in which the reconstruction of the system dynamics is performed, thus estimating three Correlation Dimensions, corresponding to Euler Angles. The effectiveness of the proposed system is assessed through several experimentations.