Leveraging Proximity Sensing to Mine the Behavior of Museum Visitors

Face-to-face proximity has been successfully leveraged to study the relationships between individuals in various contexts, from a working place, to a conference, a museum, a fair, and a date. We spend time facing the individuals with whom we chat, discuss, work, and play. However, face-to-face proximity is not the realm of solely person-to-person relationships, but it can be used as a proxy to study person-to-object relationships as well. We face the objects with which we interact on a daily basis, like a television, the kitchen appliances, a book, including more complex objects like a stage where a concert is taking place. In this paper, we focus on the relationship between the visitors of an art exhibition and its exhibits. We design, implement, and deploy a sensing infrastructure based on inexpensive mobile proximity sensors and a filtering pipeline that we use to measure face-to-face proximity between individuals and exhibits. Our pipeline produces an improvement in measurement accuracy of up to 64% relative to raw data. We use this data to mine the behavior of the visitors and show that group behavior can be recognized by means of data clustering and visualization.