Chapter 8 Correlation Dimension-Based Recognition of Simple Juggling Movements Francesco Camastra, Francesco Esposito and Antonino Staiano Abstract 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 infor- mation 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. 8.1 Introduction Several social deep impact application domains, e.g., human health, robot design, video games and virtual reality, just to name a few [15], involve the study of human movements. Nowadays, thanks to technology, there is a plethora of sensing devices for measuring and analyzing the human movements [2, 10]. Thus, an ever increasing number of applications, in particular in the e-health domain, have been developing, exploiting several kind of sensors (e.g., body sensor or wireless sensor networks). For instance, they are devoted to the recognition of elderly activities in ambient assisted living by using etherogeneous machine learning techniques [3, 6, 7, 20], involving F. Camastra ⋅ F. Esposito ⋅ A. Staiano ( ✉ ) Department of Science and Technology, University of Naples “Parthenope”, Isola C4, Centro Direzionale, 80143 Napoli, NA, Italy e-mail: staiano@ieee.org F. Camastra e-mail: camastra@ieee.org F. Esposito e-mail: francescoesposito7@gmail.com © Springer International Publishing AG 2018 A. Esposito et al. (eds.), Multidisciplinary Approaches to Neural Computing, Smart Innovation, Systems and Technologies 69, DOI 10.1007/978-3-319-56904-8_8 77