Human activity recognition applying computational intelligence techniques for fusing information related to WiFi positioning and body posture
Authors
Álvarez Álvarez, Alberto; Alonso Moral, José María; Triviño, Gracián; Hernández Parra, Noelia; Herranz Cabrilla, Fernando; [et al.]Identifiers
Permanent link (URI): http://hdl.handle.net/10017/60185DOI: 10.1109/FUZZY.2010.5584187
ISBN: 978-1-4244-8126-2
Publisher
IEEE
Date
2010-07-18Funders
Ministerio de Ciencia e Innovación
Comunidad de Madrid
Bibliographic citation
Álvarez Álvarez, A. [et al.], 2010, "Human activity recognition applying computational intelligence techniques for fusing information related to WiFi positioning and body posture", in Proceedings of the IEEE WCCI 2010, pp. 1878-1885.
Description / Notes
IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE WCCI 2010, 18/07/2010-23/07/2010, Barcelona, España.
Project
info:eu-repo/grantAgreement/MICINN//TIN2008-06890-C02-01/ES/INTERFAZ CON LOS SERES HUMANOS EN SISTEMAS DE COMPUTACION CON PALABRAS Y PERCEPCIONES EN ENTORNOS INTELIGENTES/
info:eu-repo/grantAgreement/CAM//S-2009%2FDPI-1559
Document type
info:eu-repo/semantics/conferenceObject
Version
info:eu-repo/semantics/publishedVersion
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
© 2010 IEEE
Access rights
info:eu-repo/semantics/openAccess
Abstract
This work presents a general framework for people indoor activity recognition. Firstly, a Wireless Fidelity (WiFi) localization system implemented as a Fuzzy Rulebased Classifier (FRBC) is used to obtain an approximate position at the level of discrete zones (office, corridor, meeting room, etc). Secondly, a Fuzzy Finite State Machine (FFSM) is used for human body posture recognition (seated, standing upright or walking). Finally, another FFSM combines bothWiFi localization and posture recognition to obtain a robust, reliable, and easily understandable activity recognition system (working in the desk room, crossing the corridor, having a meeting, etc). Each user carries with a personal digital agenda (PDA) or smart-phone equipped with a WiFi interface for localization task and accelerometers for posture recognition. Our approach does not require adding new hardware to the experimental environment. It relies on the WiFi access points (APs) widely available in most public and private buildings. We include a practical experimentation where good results were achieved.
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Human_Alvarez_WCCI_2010.pdf | 1.426Mb |
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