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 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.
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Notes
- 1.
The juggler in the figure is Francesco Esposito, the second paper author, who gives his consent to show his image in the paper.
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Acknowledgements
The research was developed when Francesco Esposito was at the Department of Science and Technology, University of Naples Parthenope, as B. Sc. student in Computer Science. Francesco Camastra and Antonino Staiano were funded by Sostegno alla ricerca individuale per il triennio 2015–17 project of University of Naples Parthenope. This study was exempt from ethical approval procedures since it involved health subjects which volunteered their participation. Informed consents were signed by each participant after they were debriefed on the experimental protocol, the aims of the study and the procedures.
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Camastra, F., Esposito, F., Staiano, A. (2018). Correlation Dimension-Based Recognition of Simple Juggling Movements. In: Esposito, A., Faudez-Zanuy, M., Morabito, F., Pasero, E. (eds) Multidisciplinary Approaches to Neural Computing. Smart Innovation, Systems and Technologies, vol 69. Springer, Cham. https://doi.org/10.1007/978-3-319-56904-8_8
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