Towards people indoor localization combining wifi and human motion recognition

• José Alonso ^[2] ; Alberto Alvarez ^[2] ; Gracián Triviño ^[2] ; Noelia Hernández ^[1] ; Fernando Herranz ^[1] ; Manuel Ocaña ^[1]
  1. [1] Universidad de Alcalá

     Universidad de Alcalá

     Alcalá de Henares, España

     
  2. [2] European Centre for Soft Computing
• Localización: XV Congreso Español sobre Tecnologías y Lógica Fuzzy ESTYLF 2010: Huelva [Recurso electrónico] / coord. por Antonio Peregrín Rubio, 2010, ISBN 978-84-92944-02-6, págs. 7-12
• Idioma: español
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• Resumen

  • 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.