Evaluating visitors’ experience in museum: Comparing artificial intelligence and multi-partitioned analysis

Q1 Social Sciences Digital Applications in Archaeology and Cultural Heritage Pub Date : 2024-05-01 DOI:10.1016/j.daach.2024.e00340

Sofia Ceccarelli , Amedeo Cesta , Gabriella Cortellessa , Riccardo De Benedictis , Francesca Fracasso , Laura Leopardi , Luca Ligios , Ernesto Lombardi , Saverio Giulio Malatesta , Angelo Oddi , Alfonsina Pagano , Augusto Palombini , Gianmauro Romagna , Marta Sanzari , Marco Schaerf

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Abstract

Analysing visitors' behaviour in a museum or in a cultural site is a crucial element to manage spaces and artworks arrangement as well as improving the visit experience. This paper presents the preliminary results of the ARTEMISIA project, exploiting Artificial Intelligence (AI) techniques to study, design and develop a methodology to interpret visitors' behaviour within a museum context, namely the Museum of Rome in Palazzo Braschi (Rome, Italy). The aim is to combine literature on users' experience (UX) analysis with experimental data coming from the visitor anonymous tracking out of motion sensors (users' stand-still positions, viewpoint direction, movements), merging approaches of different research domains. Through the use of agglomerative hierarchical clustering algorithms, four categories of visitors were identified, then associated to user profiles emerged by UX evaluations. Such analysis may lead to new forms of visitors profiling and to the development of a new generation of customised applications in public and private contexts. Identifying and predicting users’ patterns with respect to museum halls arrangement may also be useful to suggest improvement in the museum spaces and exhibitions (new indications, updated storytelling or changes in thematic configuration).

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评估参观者在博物馆的体验：人工智能与多分区分析的比较

分析参观者在博物馆或文化场所的行为是管理空间和艺术品布置以及改善参观体验的关键因素。本文介绍了 ARTEMISIA 项目的初步成果，该项目利用人工智能（AI）技术研究、设计和开发了一种方法，用于解释博物馆（即位于意大利罗马布拉奇宫的罗马博物馆）内参观者的行为。目的是将有关用户体验（UX）分析的文献与来自运动传感器匿名跟踪的参观者实验数据（用户的静止位置、视角方向、动作）相结合，融合不同研究领域的方法。通过使用聚类分层聚类算法，确定了游客的四个类别，然后将其与用户体验评估中出现的用户特征联系起来。这种分析可能会带来新形式的访客特征分析，并开发出新一代公共和私人领域的定制应用程序。识别和预测用户在博物馆展厅布置方面的模式也有助于提出改进博物馆空间和展览的建议（新的指示、更新的故事讲述或主题配置的变化）。保留所有权利。

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Digital Applications in Archaeology and Cultural Heritage Social Sciences-Archeology

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5.40

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