INNOVATIVE TECHNOLOGIES IN MUSEUMS: A REVIEW OF GAMIFIED AUGMENTED REALITY EXPERIENCES
DOI: 10.21125/inted.2024.1121
B. Botte, G. Marinensi, V. Malakuczi, W. Vitaletti (2024) INNOVATIVE TECHNOLOGIES IN MUSEUMS: A REVIEW OF GAMIFIED AUGMENTED REALITY EXPERIENCES, INTED2024 Proceedings, pp. 4338-4345.
The enhancement of cultural heritage experiences using technology has, by now, a long tradition. New immersive technologies, like augmented reality, further widened the plethora of possible public interactions with works of art and more, modifying the traditional visiting experience. In addition to the use of technology, gamification strategies have been also implemented, in some cases, to further support public involvement during museum visits. This scientific paper explores the landscape of innovative technologies and gamification strategies employed in the museum domain for enhancing visitor engagement and cultural heritage appreciation. The scope of this work is twofold: on one side it aims at identifying the main design trends involving gamification and immersive technologies, while, at the same time, it aims at identifying the main design aspects that can be improved. To do so, a comprehensive literature review was conducted, focusing on museum-related projects utilizing these technologies. The search covered the period from 2018 onward, considering only projects tested within museum exhibitions and leveraging cutting-edge technologies. The predominant platform for engagement was found to be mobile devices, with 13 out of 19 projects integrating mobile applications. The paper delves into detailed case studies, including interactive projections and augmented reality experiences, and insights are drawn regarding user involvement, ease of use, and the duration of experiences within museum settings. The analysis of augmented reality experiences underscores the potential of these technologies in enhancing visitor satisfaction, learning, and narrative engagement. The paper also examines user involvement, challenges faced in implementation, and the delicate balance between entertainment and educational aspects. The findings reveal positive attitudes toward augmented reality applications, with a focus on user engagement and cognitive involvement. However, challenges related to user attention and the potential overshadowing of real exhibits by digital components are acknowledged. The importance of user-friendly interfaces, effective storytelling, and addressing technological limitations is emphasized. The study provides valuable insights for the design and implementation of technology-driven museum experiences, with considerations for improving user interaction, educational outcomes, and overall visitor satisfaction.
A Machine Learning approach for personal thermal comfort perception evaluation: experimental campaign under real and virtual scenarios
DOI: 10.1051/e3sconf/202019704001
Salamone, Francesco & Bellazzi, Alice & Belussi, Lorenzo & Damato, Gianfranco & Danza, Ludovico & Dell’aquila, Federico & Ghellere, Matteo & Megale, Valentino & Meroni, Italo & Vitaletti, Walter. (2020). A Machine Learning approach for personal thermal comfort perception evaluation: experimental campaign under real and virtual scenarios. E3S Web of Conferences. 197 (2020). 10.1051/e3sconf/202019704001.
Personal Thermal Comfort models differ from the steady-state methods because they consider personal user feedback as target value. Today, the availability of integrated “smart” devices following the concept of the Internet of Things and Machine Learning (ML) techniques allows developing frameworks reaching optimized indoor thermal comfort conditions. The article investigates the potential of such approach through an experimental campaign in a test cell, involving 25 participants in a Real (R) and Virtual (VR) scenario, aiming at evaluating the effect of external stimuli on personal thermal perception, such as the variation of colours and images of the environment. A dataset with environmental parameters, biometric data and the perceived comfort feedbacks of the participants is defined and managed with ML algorithms in order to identify the most suitable one and the most influential variables that can be used to predict the Personal Thermal Comfort Perception (PTCP). The results identify the Extra Trees classifier as the best algorithm. In both R and VR scenario a different group of variables allows predicting PTCP with high accuracy.
Evaluation of the Visual Stimuli on Personal Thermal Comfort Perception in Real and Virtual Environments Using Machine Learning Approaches
Salamone, Francesco & Bellazzi, Alice & Belussi, Lorenzo & Damato, Gianfranco & Danza, Ludovico & Dell’Aquila, Federico & Ghellere, Matteo & Megale, Valentino & Meroni, Italo & Vitaletti, Walter. (2020). Evaluation of the Visual Stimuli on Personal Thermal Comfort Perception in Real and Virtual Environments Using Machine Learning Approaches. Sensors. 20. 10.3390/s20061627.
DOI: 10.3390/s20061627
Personal Thermal Comfort models consider personal user feedback as a target value. The growing development of integrated “smart” devices following the concept of the Internet of Things and data-processing algorithms based on Machine Learning techniques allows developing promising frameworks to reach the best level of indoor thermal comfort closest to the real needs of users. The article investigates the potential of a new approach aiming at evaluating the effect of visual stimuli on personal thermal comfort perception through a comparison of 25 participants’ feedback exposed to a real scenario in a test cell and the same environment reproduced in Virtual Reality. The users’ biometric data and feedback about their thermal perception along with environmental parameters are collected in a dataset and managed with different Machine Learning techniques. The most suitable algorithm, among those selected, and the influential variables to predict the Personal Thermal Comfort Perception are identified. The Extra Trees classifier emerged as the most useful algorithm in this specific case. In real and virtual scenarios, the most important variables that allow predicting the target value are identified with an average accuracy higher than 0.99.