Fine-arts museums design exhibitions to educate, inform and entertain visitors. Existing work leverages technology to engage, guide and interact with the visitors, neglecting the need of museum staff to understand the response of the visitors. Surveys and expensive observational studies are currently the only available data source to evaluate visitor behavior, with limits of scale and bias. In this paper, we explore the use of data provided by low-cost mobile and fixed proximity sensors to understand the behavior of museum visitors. We present visualizations of visitor behavior, and apply both clustering and prediction techniques to the collected data to show that group behavior can be identified and leveraged to support the work of museum staff.
Authors: Claudio Martella, Armando Miraglia, Jeana Frost, Marco Cattani, Maarten van Steen
Research group: Large-scale Distributed Systems group, VU University Amsterdam and Department of Communication Sciences at VU University Amsterdam and Embedded Software group, Delft University of Technology
Journal: Pervasive and Mobile Computing