Resumen de Towards people indoor localization combining wifi and human motion recognition

José M. Alonso Moral , Alberto Alvarez Alvarez, Gracián Triviño Barros , Noelia Hernández Parra , Fernando Herránz Cabrilla, Manuel Ocaña Miguel

• 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 environments. 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 finding out whether people is (or not) moving, in which direction, at which pace, etc. Finally, another FFSM combines both WiFi localization and human motion recognition with the aim of obtaining a robust, reliable, and easily understandable human-oriented localization system.