Abstract
In this paper we present a method of the user behavior (UB) tracking by capturing and measuring user activities through the defined procedural model of the reverse virtualization process, implementing a proof of concept on a real case scenario: the Civic Gallery of Ascoli. In order to define the universal model of such “vice versa” virtual reality (VR) experience, we assigned particular descriptive functions (descriptors) to each interactive feature of the virtual user space. In this virtualization phase we store user interaction information locally using the web-socket streams protocol, ensuring complete control and manipulation of monitored functions. Our algorithm firstly collects the user interaction data and extracts the descriptors’ arguments into the indexed vector of corresponding variables. The next step determines UB pattern by solving the inverse descriptive functions in combination with an appropriate statistical analysis of gathered data. The final result of the proposed method is the repository of salient data that is used in the further user experience improvement, as well as to enable the museums to distinguish the most important points of the visitor interest in the virtual web tours. Our approach also offers a potential benefit of obtained results in an automatic calculation and prediction of UB patterns using artificial intelligence (AI).
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Acknowledgments
The authors acknowledge the research presented here is part of Project V.I.T.A. (Virtual Immersion in Territorial Arts), funded by the Marche region and object of the grants among the Dep. DICEA and the cooperatives PULCHRA, TOGHETER WE CARE and GREEN PARK. The authors would like to thank also to the Director of the Civic Gallery of Ascoli, Prof. Stefano Papetti.
The panoramic acquisition and processing were carried out by Luigi Sagone and the TLS acquisition by Floriano Capponi.
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Vasic, I. et al. (2022). Virtualization and Vice Versa: A New Procedural Model of the Reverse Virtualization for the User Behavior Tracking in the Virtual Museums. In: De Paolis, L.T., Arpaia, P., Sacco, M. (eds) Extended Reality. XR Salento 2022. Lecture Notes in Computer Science, vol 13446. Springer, Cham. https://doi.org/10.1007/978-3-031-15553-6_23
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