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Article

User-Centered Predictive Model for Improving Cultural Heritage Augmented Reality Applications: An HMM-Based Approach for Eye-Tracking Data

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Dipartimento di Ingegneria Civile, Edile e dell’Architettura, Universitá Politecnica delle Marche, 60131 Ancona, Italy
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Dipartimento di Ingegneria dell’Informazione, Universitá Politecnica delle Marche, 60131 Ancona, Italy
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Department of Materials, Environmental Sciences and Urban Planning, Universitá Politecnica delle Marche, 60131 Ancona, Italy
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Department of Agricultural, Food and Environmental Sciences, Universitá Politecnica delle Marche, 60131 Ancona, Italy
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Author to whom correspondence should be addressed.
J. Imaging 2018, 4(8), 101; https://doi.org/10.3390/jimaging4080101
Received: 25 June 2018 / Revised: 27 July 2018 / Accepted: 1 August 2018 / Published: 6 August 2018
(This article belongs to the Special Issue Multimedia Content Analysis and Applications)
Today, museum visits are perceived as an opportunity for individuals to explore and make up their own minds. The increasing technical capabilities of Augmented Reality (AR) technology have raised audience expectations, advancing the use of mobile AR in cultural heritage (CH) settings. Hence, there is the need to define a criteria, based on users’ preference, able to drive developers and insiders toward a more conscious development of AR-based applications. Starting from previous research (performed to define a protocol for understanding the visual behaviour of subjects looking at paintings), this paper introduces a truly predictive model of the museum visitor’s visual behaviour, measured by an eye tracker. A Hidden Markov Model (HMM) approach is presented, able to predict users’ attention in front of a painting. Furthermore, this research compares users’ behaviour between adults and children, expanding the results to different kind of users, thus providing a reliable approach to eye trajectories. Tests have been conducted defining areas of interest (AOI) and observing the most visited ones, attempting the prediction of subsequent transitions between AOIs. The results demonstrate the effectiveness and suitability of our approach, with performance evaluation values that exceed 90%. View Full-Text
Keywords: hidden markov models; eye-tracking; augmented reality applications; cultural heritage hidden markov models; eye-tracking; augmented reality applications; cultural heritage
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MDPI and ACS Style

Pierdicca, R.; Paolanti, M.; Naspetti, S.; Mandolesi, S.; Zanoli, R.; Frontoni, E. User-Centered Predictive Model for Improving Cultural Heritage Augmented Reality Applications: An HMM-Based Approach for Eye-Tracking Data. J. Imaging 2018, 4, 101. https://doi.org/10.3390/jimaging4080101

AMA Style

Pierdicca R, Paolanti M, Naspetti S, Mandolesi S, Zanoli R, Frontoni E. User-Centered Predictive Model for Improving Cultural Heritage Augmented Reality Applications: An HMM-Based Approach for Eye-Tracking Data. Journal of Imaging. 2018; 4(8):101. https://doi.org/10.3390/jimaging4080101

Chicago/Turabian Style

Pierdicca, Roberto, Marina Paolanti, Simona Naspetti, Serena Mandolesi, Raffaele Zanoli, and Emanuele Frontoni. 2018. "User-Centered Predictive Model for Improving Cultural Heritage Augmented Reality Applications: An HMM-Based Approach for Eye-Tracking Data" Journal of Imaging 4, no. 8: 101. https://doi.org/10.3390/jimaging4080101

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