Robotics and eSafety Research Group

This work presents a general framework for people indoor localization. Firstly, a WiFi localization system implemented as a fuzzy rule-based classifier (FRBC) is used to deal with the intrinsic uncertainty of such envi- ronments. It consists of a set of linguistic variables and rules automatically generated from experimental data. As a result, it yields an approximate position at the level of discrete zones (room, corridor, toilet, etc). Secondly, a Fuzzy Finite State Machine (FFSM) mainly based on expert knowledge is used for human motion (activity, body posture and step length) recognition. The goal is find- ing out whether people is (or not) moving, in which direction, at which pace, etc. Finally, another FFSM combines bothWiFi localiza- tion and human motion recognition with the aim of obtaining a robust, reliable, and eas- ily understandable human-oriented localiza- tion system.