Use of digital guides in museum galleries: Determinants of information selection

Eghbal-Azar, Kira; Merkt, Martin; Bahnmueller, Julia; Schwan, Stephan

doi:10.1016/j.chb.2015.12.035

Cited by 28 publications

(21 citation statements)

References 35 publications

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“…Empirical research has demonstrated the impact of an exhibit's presentation context on visitors' behavior. Besides its overall spatial position in a gallery (Eghbal‐Azar, Merkt, Bahnmueller, & Schwan, 2016; Turgay Zıraman & Imamoğlu, 2018), size and attractivity of other exhibits in the immediate surrounding may strongly influence how much attention visitors pay to a particular authentic object (Turgay Zıraman & Imamoğlu, 2018). More importantly, providing labels and explaining texts has been shown to prolong the visitors' inspection time, guide their attention, and foster a deeper understanding of an object (Schwan, Dutz, & Dreger, 2018).…”

Section: Introductionmentioning

confidence: 99%

How do Visitors Perceive the Role of Authentic Objects in Museums?

Schwan¹,

Dutz²

2020

Curator The Museum Journal

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The study investigated visitors’ notions of authentic objects across nine different German museums, three science and technology, three natural history, and three cultural history. The findings build on prior studies into visitors’ conceptions of authenticity of artifacts in science museums, indicating a nuanced role of authentic objects for museum exhibitions from the visitors' perspective. The results were generally consistent across all three museum types. Visitors emphasized cognitive effects of authentic objects, wanted authentic objects to be accompanied by additional information, and considered replicas to be legitimate substitutes. Visitors indicated that they accept that authentic objects could include modifications or improvements if the original object contained elements that were fragile or subject to harm from their use in an exhibition setting.

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Section: Introductionmentioning

confidence: 99%

How do Visitors Perceive the Role of Authentic Objects in Museums?

Schwan¹,

Dutz²

2020

Curator The Museum Journal

Self Cite

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“…In other words, it is very likely that once eyes observe certain AOIs, they continue observing those same AOIs also in the next instant. Furthermore, we can see other elements, not along the diagonal, with significant probability values such as the elements (1,5), (1,6), (5,3) because the user has a high probability to focus his/her attention passing from the center of the painting to the AOI in the corners. Figure 8b depicts the modelling of the transition matrix of the total recorded period for the children.…”

Section: Resultsmentioning

confidence: 99%

“…In addition, nowadays museums have a unique perspective on technology's impact. A modern approach to the fruition of art is actually based on a wide and targeted use of technologies [1,2] and a growing number of museums are adopting digital tools as an integral part of the exhibition, providing users new instruments to study art deeply [3,4]. Among others, the increasing technical capabilities of Augmented Reality (AR) have raised audience expectations, advancing the use of mobile AR in CH settings [5].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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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%.

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“…When a visitor comes to a heritage site, there are two possibilities: they are interested in some particular civilization, or time period, or artist; or they are present to explore what is on display and do not have any specific preferences. IoT-based systems can be used to interact with the visitors and guide them around the premises [ 16 ]. At a personal level, a user will be more comfortable with a museum guide which can provide unbiased and correctly sourced historical background, can be turned off when required.…”

Section: Related Workmentioning

confidence: 99%

ARTYCUL: A Privacy-Preserving ML-Driven Framework to Determine the Popularity of a Cultural Exhibit on Display

Tanwar

Chauhan

Yafi

2021

Sensors

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We present ARTYCUL (ARTifact popularitY for CULtural heritage), a machine learning(ML)-based framework that graphically represents the footfall around an artifact on display at a museum or a heritage site. The driving factor of this framework was the fact that the presence of security cameras has become universal, including at sites of cultural heritage. ARTYCUL used the video streams of closed-circuit televisions (CCTV) cameras installed in such premises to detect human figures, and their coordinates with respect to the camera frames were used to visualize the density of visitors around the specific display items. Such a framework that can display the popularity of artifacts would aid the curators towards a more optimal organization. Moreover, it could also help to gauge if a certain display item were neglected due to incorrect placement. While items of similar interest can be placed in vicinity of each other, an online recommendation system may also use the reputation of an artifact to catch the eye of the visitors. Artificial intelligence-based solutions are well suited for analysis of internet of things (IoT) traffic due to the inherent veracity and volatile nature of the transmissions. The work done for the development of ARTYCUL provided a deeper insight into the avenues for applications of IoT technology to the cultural heritage domain, and suitability of ML to process real-time data at a fast pace. While we also observed common issues that hinder the utilization of IoT in the cultural domain, the proposed framework was designed keeping in mind the same obstacles and a preference for backward compatibility.

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Use of digital guides in museum galleries: Determinants of information selection

Cited by 28 publications

References 35 publications

How do Visitors Perceive the Role of Authentic Objects in Museums?

How do Visitors Perceive the Role of Authentic Objects in Museums?

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

ARTYCUL: A Privacy-Preserving ML-Driven Framework to Determine the Popularity of a Cultural Exhibit on Display

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