Knowing in real-time the position of objects and people, both in indoor and outdoor spaces, allows companies and organizations to improve their processes and offer new kind of services. Nowadays Location-based Services (LBS) generate a significant amount of data thank to the widespread of the Internet of Things; since they have been quickly perceived as a potential source of profit, several companies have started to design and develop a wide range of such services. One of the most challenging research tasks is undoubtedly represented by the analysis of LBS data through Machine Learning algorithms and methodologies in order to infer new knowledge and build-up even more customized services. Cultural Heritage is a domain that can benefit from such studies since it is characterized by a strong interaction between people, cultural items and spaces. Data gathered in a museum on visitor movements and behaviours can constitute the knowledge base to realize an advanced monitoring system able to offer museum stakeholders a complete and real-time snapshot of the museum locations occupancy. Furthermore, exploiting such data through Deep Learning methodologies can lead to the development of a predictive monitoring system able to suggest stakeholders the museum locations occupancy not only in real-time but also in the next future, opening new scenarios in the management of a museum. In this paper, we present and discuss a Deep Learning methodology applied to data coming from a non-invasive Bluetooth IoT monitoring system deployed inside a cultural space. Through the analysis of visitors’ paths, the main goal is to predict the occupancy of the available rooms. Experimental results on real data demonstrate the feasibility of the proposed approach; it can represent a useful instrument, in the hands of the museum management, to enhance the quality-of-service within this kind of spaces.

实时了解室内和室外空间中物体和人员的位置，使公司和组织可以改善其流程并提供新型服务。如今，由于物联网的广泛普及，基于位置的服务（LBS）产生了大量数据；由于已迅速将它们视为潜在的利润来源，因此多家公司已开始设计和开发各种此类服务。毫无疑问，最具挑战性的研究任务之一是通过机器学习算法和方法论对LBS数据进行分析，以推断出新知识并建立更多定制服务。文化遗产是一个可以从此类研究中受益的领域，因为它的特点是人，文化物品和空间之间的强大互动。博物馆中收集的有关游客活动和行为的数据可以构成知识库，以实现高级监控系统，该系统可以为博物馆利益相关者提供博物馆位置占用情况的完整实时快照。此外，通过深度学习方法利用此类数据可以导致开发一种预测性监控系统，该系统不仅可以实时向利益相关者建议博物馆位置占用情况，而且还可以在未来的将来为博物馆管理人员打开新的场景。在本文中，我们介绍并讨论了一种深度学习方法，该方法适用于来自部署在文化空间内的非侵入式蓝牙IoT监控系统的数据。通过分析访客的路径，主要目标是预测可用房间的占用情况。真实数据的实验结果证明了该方法的可行性。在博物馆管理方面，它可以代表一种有用的工具，可以提高此类空间的服务质量。