IoT and Engagement in the Ubiquitous Museum.

IoT mobile sensors museum behaviour prediction museum visitor analysis space sensing visitor attention visitor engagement

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
21 Mar 2019
Historique:
received: 04 02 2019
revised: 07 03 2019
accepted: 14 03 2019
entrez: 24 3 2019
pubmed: 25 3 2019
medline: 25 3 2019
Statut: epublish

Résumé

In increasingly hyper-connected societies, where individuals rely on short and fast online communications to consume information, museums face a significant survival challenge. Collaborations between scientists and museums suggest that the use of the technological framework known as Internet of Things (IoT) will be a key player in tackling this challenge. IoT can be used to gather and analyse visitor generated data, leading to data-driven insights that can fuel novel, adaptive and engaging museum experiences. We used an IoT implementation-a sensor network installed in the physical space of a museum-to look at how single visitors chose to enter and spend time in the different rooms of a curated exhibition. We collected a sparse, non-overlapping dataset of individual visits. Using various statistical analyses, we found that visitor attention span was very short. People visited five out of twenty rooms on average, and spent a median of two minutes in each room. However, the patterns of choice and time spent in rooms were not random. Indeed, they could be described in terms of a set of linearly separable visit patterns we obtained using principal component analysis. These results are encouraging for future interdisciplinary research that seeks to leverage IoT to get numerical proxies for people attention inside the museum, and use this information to fuel the next generation of possible museum interactions. Such interactions will based on rich, non-intrusive and diverse IoT driven conversation, dynamically tailored to visitors.

Identifiants

pubmed: 30901817
pii: s19061387
doi: 10.3390/s19061387
pmc: PMC6470879
pii:
doi:

Types de publication

Journal Article

Langues

eng

Références

Br J Math Stat Psychol. 2007 Nov;60(Pt 2):295-314
pubmed: 17971271

Auteurs

Roberto Pierdicca (R)

DICEA Universitá Politecnica delle Marche Ancona, 60131 Ancona, Italy. r.pierdicca@staff.univpm.it.

Manuel Marques-Pita (M)

CICANT, Universidade Lusófona (ULHT), 1700-097 Lisbon, Portugal. manuel.pita@ulusofona.pt.

Marina Paolanti (M)

Department of Information Engineering, DII, Universitá Politecnica delle Marche Ancona, 60131 Ancona, Italy. m.paolanti@staff.univpm.it.

Eva Savina Malinverni (ES)

DICEA Universitá Politecnica delle Marche Ancona, 60131 Ancona, Italy. e.s.malinverni@staff.univpm.it.

Classifications MeSH