The User Model, Vocabulary and Logical Architecture for Adaptive Augmented Reality

Tenemaza, Maritzol; Jiménez, Antonio; Ramírez, Jaime

doi:10.1145/2824893.2824901

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…Adaptive Augmented Reality (AAR) is an augmented reality application that provides valuable and effective real-time information based on the user's unique characteristics, interests, and context [13], [20]. [10] study AAR in the conception of plasticity in HCI.…”

Section: Adaptive Augmented Realitymentioning

confidence: 99%

“…Shiratuddin and Tenh [26] examined several previous works on adaptive augmented reality to establish the adaptive concept's core knowledge elements. Components of such a concept were gathered and offered a formal definition using model analysis, covering four models.These four models are A2R Museum Augmentation Model by Damala and Stojanovic [13], Conceptual framework for Adaptive Multimodal Interfaces in Mobile Augmented Reality by Abidin, Arshad, and Shukri [11], Conceptual framework for Adaptive Augmented Reality by Tenemaza et al [20] and last but not least, i-ARA Adaptive Augmented Reality by Hervás et al [27].…”

Section: Aar Core Componentsmentioning

confidence: 99%

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Components of Adaptive Augmented Reality Model for Heritage Mobile Application

Tenh

Shiratuddin

2022

Int. J. Interact. Mob. Technol.

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Adaptive augmented reality (AAR) is the latest concept of the augmented reality that responds and adapts to a real-time context and thecharacteristics of the users. This concept is said to provide adaptation of 3Daugmented reality, better engagement and more emotional experience to theusers. In AAR, the information is provided to the users through interaction tracking and sensor on the device. However, this technology is still in itsinfancy. Therefore, a study to explore positive ways of providing relevantinformation that can enhance user experience is urgently required. Theobjective of this paper is to identify the components of an AAR model forheritage mobile application. It is hypothesized that such model would enhance user experience and provide better engagement. Consequently, models from past studies on Augmented Reality, UX Design and storytelling are reviewed. Findings indicate that personalization and emotion are among the important components. Inclusion of these components seems to allow users to be more engaged and inspired to learn about the cultural heritage, hence enhances user experience.

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Section: Adaptive Augmented Realitymentioning

confidence: 99%

Section: Aar Core Componentsmentioning

confidence: 99%

Components of Adaptive Augmented Reality Model for Heritage Mobile Application

Tenh

Shiratuddin

2022

Int. J. Interact. Mob. Technol.

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“…The more important module is the evolutionary optimizer presented by the researchers, in which Tarantino et al propose a user model to register the users' information. Conversely, we propose a complete model with analysis of the user [18], [19]. We have not included this analysis because we have focused on the optimization algorithm.…”

Section: Related Workmentioning

confidence: 99%

Improving Itinerary Recommendations for Tourists Through Metaheuristic Algorithms: An Optimization Proposal

et al. 2020

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In recent years, recommender systems have been used as a solution to support tourists with recommendations oriented to maximize the entertainment value of visiting a tourist destination. However, this is not an easy task because many aspects need to be considered to make realistic recommendations: the context of a tourist destination visited, lack of updated information about points of interest, transport information, weather forecast, etc. The recommendations concerning a tourist destination must be linked to the interests and constraints of the tourist. In this research, we present a mobile recommender system based on Tourist Trip Design Problem (TTDP)/Time Depending (TD)-Orienteering Problem (OP)-Time Windows (TW), which analyzes in real time the user's constraints and the points of interest's constraints. For solving TTDP, we clustered preferences depending on the number of days that a tourist will visit a tourist destination using a k-means algorithm. Then, with a genetic algorithm (GA), we optimize the proposed itineraries to tourists for facilitating the organization of their visits. We also used a parametrized fitness function to include any element of the context to generate an optimized recommendation. Our recommender is different from others because it is scalable and adaptable to environmental changes and users' interests, and it offers real-time recommendations. To test our recommender, we developed an application that uses our algorithm. Finally, 131 tourists used this recommender system and an analysis of users' perceptions was developed. Metrics were also used to detect the percentage of precision, in order to determine the degree of accuracy of the recommender system. This study has implications for researchers interested in developing software to recommend the best itinerary for tourists with constraint controls with regard to the optimized itineraries.

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PARSAT: Fuzzy logic for adaptive spatial ability training in an augmented reality system

Papakostas,

Troussas,

Krouska

et al. 2023

ComSIS

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Personalized training systems and augmented reality are two of the most promising educational technologies since they could enhance engineering students? spatial ability. Prior research has examined the benefits of the integration of augmented reality in increasing students? motivation and enhancing their spatial skills. However, based on the review of the literature, current training systems do not provide adaptivity to students? individual needs. In view of the above, this paper presents a novel adaptive augmented reality training system, which teaches the knowledge domain of technical drawing. The novelty of the proposed system is that it proposes using fuzzy sets to represent the students? knowledge levels more accurately in the adaptive augmented reality training system. The system determines the amount and the level of difficulty of the learning activities delivered to the students, based on their progress. The main contribution of the system is that it is student-centered, providing the students with an adaptive training experience. The evaluation of the system took place during the 2021-22 and 2022-23 winter semesters, and the results are very promising.

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The User Model, Vocabulary and Logical Architecture for Adaptive Augmented Reality

Cited by 4 publications

References 9 publications

Components of Adaptive Augmented Reality Model for Heritage Mobile Application

Components of Adaptive Augmented Reality Model for Heritage Mobile Application

Improving Itinerary Recommendations for Tourists Through Metaheuristic Algorithms: An Optimization Proposal

PARSAT: Fuzzy logic for adaptive spatial ability training in an augmented reality system

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