Towards people indoor localization combining wifi and human motion recognition

  1. José Alonso 1
  2. Alberto Alvarez 1
  3. Gracián Triviño 1
  4. Noelia Hernández 2
  5. Fernando Herranz 2
  6. Manuel Ocaña 2
  1. 1 European Centre for Soft Computing
    info

    European Centre for Soft Computing

    Mieres del Camino, España

  2. 2 Universidad de Alcalá
    info

    Universidad de Alcalá

    Alcalá de Henares, España

    ROR https://ror.org/04pmn0e78

Libro:
XV Congreso Español sobre Tecnologías y Lógica Fuzzy ESTYLF 2010: Huelva [Recurso electrónico]
  1. Peregrín Rubio, Antonio (coord.)

Editorial: Universidad de Huelva

ISBN: 978-84-92944-02-6

Ano de publicación: 2010

Páxinas: 7-12

Congreso: Congreso Español sobre Tecnologías y Lógica Fuzzy (15. 2010. Punta Umbría)

Tipo: Achega congreso

DIALNET GOOGLE SCHOLAR

Resumo

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.

Fonte de datos: Dialnet