Human behavior monitoring using a passive indoor positioning system: a case study in a SME

The widespread use of mobile devices such as laptops, smartphones or tablets enables new opportunities and services in the field of pervasive computing and sensing. In particular, monitoring the activity of those devices in indoor working environments enables new methods to address some issues related to energy consumption or employees’ wellness. However, it is possible also to infer data about the behavioral pattern of the staff in order to increase productivity, for example identifying anomalies in working teams or unusual behaviors of some employees. In this paper we present a case study for a SME (Small Medium Enterprise) with 20 employees distributed in 5 working teams that develop their daily work in a two-floors building with a WiFi-based passive localization system. An initial analysis of the 802.11 radio signals collected by the system determines, with a high accuracy rate, which mobile devices among the thousands of recorded MAC addresses belong to employees. Additionally, making use of the localization engine, we are able to identify working patterns for the different working teams that, consequently, open the way for implementing efficient anomaly detection techniques.