AMBIENTUM BIOETHICA BIOLOGIA CHEMIA DIGITALIA DRAMATICA EDUCATIO ARTIS GYMNAST. ENGINEERING EPHEMERIDES EUROPAEA GEOGRAPHIA GEOLOGIA HISTORIA HISTORIA ARTIUM INFORMATICA IURISPRUDENTIA MATHEMATICA MUSICA NEGOTIA OECONOMICA PHILOLOGIA PHILOSOPHIA PHYSICA POLITICA PSYCHOLOGIA-PAEDAGOGIA SOCIOLOGIA THEOLOGIA CATHOLICA THEOLOGIA CATHOLICA LATIN THEOLOGIA GR.-CATH. VARAD THEOLOGIA ORTHODOXA THEOLOGIA REF. TRANSYLVAN
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Autori: .
This paper analyses a natural interface sensor-based gesture recognition for the purpose of capturing and using indirect user input during gaming and create a more personalized and enjoyable experience. We have compared 38 classifiers on our own database of 30 different body postures and analyzed the results for the best performing of these, in terms of precision, accuracy and time. We have found that the best performing classifiers to use in a real-time system are SimpleLogistic, MultiClassClassifier and RandomForest. Also, next steps are discussed in terms of combining methods for more complex poses and gestures detection, extending the database of body postures and exploring the prediction potential of such a system.
Keywords and phrases: gesture recognition, AI, Kinect, personalized games.
2010 Mathematics Subject Classification. 68T50, 68T05.
