AI-supported building monitoring for visitor management - a contribution to safely coexisting at universities during the COVID-19 pandemic
The aim of the project is to use smartphones or tablets, along with what are called wearables, to develop an intelligent visitor management system for the management of buildings and properties.
Measures that enable monitoring of visitor numbers and movement flows for compliance with hygiene concepts in large-scale buildings have become highly relevant as a result of the coronavirus pandemic. Using entrance controls provides insufficient information about the distribution and location of visitors. Comprehensive monitoring by cameras would not only be technically very elaborate and complex, it would also be in conflict with the safeguarding of personal rights.
The GEMEINSAM project builds on the use of networked sensors – familiar from concepts from the Internet of Things (IoT) and smart city or smart home sectors. The goal is to not only use smartphones and tablets, but also the ever-increasing variety of what are called wearables, which include smartwatches, fitness wristbands and Bluetooth headphones, to develop an intelligent management system for visitors and facility managers.
The results of the analysis are compiled in a web dashboard for users, which could be university administrators or students. The intelligent visitor management system is not only able to display static information of one or more buildings, it primarily offers possibilities to present real-time information, such as the number of people currently in buildings, floors or rooms. Alternatively, flows or hotspots of people in the building could be visualized. Visitors can retrieve information about buildings and their occupancy levels in advance, or be automatically notified when the maximum number of people has been exceeded.
The intelligent visitor management system is designed in such a way that the required knowledge about the number of people in a particular environment can be determined. Machine learning (ML) techniques are used to recognize patterns from input data that allow conclusions to be drawn about the current and future number of people in the rooms, enabling smart building and visitor monitoring.