Abstract
The Mobile Five Senses Augmented Reality System for Museums (M5SAR) project aims to development an Augmented Reality mobile system for museums. Museums are amazing places, where it is important to sensorial augment as much as possible the visits, permitting to see, ear, touch, smell, and taste all the interesting objects there exist. Also fundamental is that visitors with different profiles (child, adult, expert, etc.) can have different experiences when visiting the same object. In the M5SAR system, the visitor uses its smartphone to select the object(s) to explore, and the user interface adapts on-the-fly to the object(s) and user's profile. Simultaneously, when integrated, a paired hardware device allows the extension of the augmented reality system to the human five senses, complementing the visual and auditory information about the objects. This chapter presents the initial framework to develop a five senses mobile adaptive museum system.
TopIntroduction
The Mobile Five Senses Augmented Reality System for Museums (M5SAR) project aims at the development of an enhanced Augmented Reality (AR) system, to be a guide in cultural, historical and museum events, complementing or replacing the traditional orientation given by tour guides, directional signs, or maps. The system consists of a (i) smartphone/phablet application (APP) and a (ii) hardware device (referenced as HDevice) to be integrated with smartphones/phablets, in order to explore the five human senses: sight, hearing, touch, smell, and taste. Both components, (i) and (ii), can work in stand-alone or integrated fashion.
Nowadays, many personal and context-aware tourism and cultural experiences are constructed based on mobile APPs (Jung, Chung, & Leue, 2015), including the ones that use AR. Those APPs are increasing in number due to the popularity of built-in cameras, global positioning systems, and with the massive availability of Internet connections. On the other hand, most of the present User Interfaces (UI) still traditionally follow a one-size-fits-all model, typically ignoring the needs, abilities, and preferences of individual users. However, past research pointed out that visualization performance could be improved by adapting some of its aspects to the individual user (Steichen, Conati & Carenini, 2014). Conati, Carenini, Toker, and Lallé (2015) state that intelligent adaptive user interfaces (AUI) and/or visualizations, that can adapt on-the-fly to the specific needs and abilities of each individual user, are a long-term research goal. This is due to two main difficulties: (a) the extraction of information about the users’ needs and abilities and (b) the implementation of UIs that can adapt/change “themselves” on-the-fly.
Reinecke and Bernstein (2013) refer that a modular UI that allows a flexible composition of various interface elements increases the number of variations of the interface element to the power of the number of adaptable elements. Thus, instead of designing each interface from scratch, a modular user interface approach is possibly a better solution, once it allows achieving many more versions with less design effort. Therefore, the requirement changes to the creation of different designs for all those parts of the interface that are subject to user or cultural preferences. Equal importance should be given to the UI adaptation to users with different impairments. Unfortunately, because of the great variety of existing impairments, it is expectable that manually and, probably, modular designing interfaces, for each one of those impairments, is impractical and not scalable (Gajos, Wobbrock, & Weld, 2008; Rodrigues, Lessa, Gregório, Ramos, & Cardoso, 2016). Nevertheless, the modular and/or adaptive generation of UIs offers the promise of providing personalized interfaces on-the-fly, although this does not mean that the user will be satisfied with his/her personalized APP. According to Zhao, Lu, Zhang, and Chau (2012), the psychological process behind satisfaction is highly complex and requires a differentiation between transaction-specific satisfaction and cumulative satisfaction. Transaction-specific satisfaction is the judgment of an experienced service encounter at a specific point in time, whereas cumulative satisfaction is the result of “the overall evaluation of all services encountered over time”. Augmented reality exacerbates some of the challenges mentioned before because multiple types of augmentation are possible at once, and proactive APPs run the risk of overwhelming users.
Having the above in mind, at least three main challenges arise in the (i) APP implementation, i.e., in the AUI design and implementation: (a) How to harvest the necessary information about the preferences and skills of each user; (b) From the acquired information/data, how to give “intelligence” to the UI implementation so it will adapt on-the-fly to the user’s specificities; (c) How to develop the AUI, even a modular UI, without being necessary to develop a huge amount of different (sub-)modules and, at the same time, still optimize the user’s experience and the main principles of interaction design.
Key Terms in this Chapter
Multi-Sensorial Display: An output device that conveys information for multiple sensory systems.
Augmented Reality: A system that supplements the real world with virtual (computer-generated) objects that appear to coexist in the same space as the real world.
Accessible Tourism: The set of services and facilities that can allow people with specific needs the enjoyment of the holiday and leisure without obstacles and difficulties.
Human-Computer Interaction: Researches the design and use of computer technology, focused on the interfaces between humans and computers. Researchers both observe the ways in which humans interact with computers and design technologies that let humans interact with computers in novel ways.
Interaction Design: The practice of designing interactive digital products, environments, systems, and service.
Computer Vision: An interdisciplinary field that deals with how computers can gain high-level understanding from digital images or videos.