Full text
Preview |
PDF
- Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (2MB) | Preview |
Martín Rodríguez, Henar, Iglesias Alvarez, Josué, Cano García, Jesús, Bernardos Barbolla, Ana M. and Casar Corredera, José Ramón ORCID: https://orcid.org/0000-0003-3851-9038
(2012).
Towards a fuzzy-based multi-classifier selection module for activity recognition applications.
In: "4th International Workshop on Sensor Networks and Ambient Intelligence 2012", 23/03/2012 - 23/03/2012, Lugano. ISBN 978-1-4244-9529-0. pp. 871-876.
https://doi.org/10.1109/PerComW.2012.6197634.
Title: | Towards a fuzzy-based multi-classifier selection module for activity recognition applications |
---|---|
Author/s: |
|
Item Type: | Presentation at Congress or Conference (Article) |
Event Title: | 4th International Workshop on Sensor Networks and Ambient Intelligence 2012 |
Event Dates: | 23/03/2012 - 23/03/2012 |
Event Location: | Lugano |
Title of Book: | 4th International Workshop on Sensor Networks and Ambient Intelligence 2012 |
Date: | March 2012 |
ISBN: | 978-1-4244-9529-0 |
Subjects: | |
Freetext Keywords: | activity recognition, mobile technologies, context awareness, personal health applications, fuzzy inference. |
Faculty: | E.T.S.I. Telecomunicación (UPM) |
Department: | Señales, Sistemas y Radiocomunicaciones |
Creative Commons Licenses: | Recognition - No derivative works - Non commercial |
Preview |
PDF
- Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (2MB) | Preview |
Performing activity recognition using the information provided by the different sensors embedded in a smartphone face limitations due to the capabilities of those devices when the computations are carried out in the terminal. In this work a fuzzy inference module is implemented in order to decide which classifier is the most appropriate to be used at a specific moment regarding the application requirements and the device context characterized by its battery level, available memory and CPU load. The set of classifiers that is considered is composed of Decision Tables and Trees that have been trained using different number of sensors and features. In addition, some classifiers perform activity recognition regardless of the on-body device position and others rely on the previous recognition of that position to use a classifier that is trained with measurements gathered with the mobile placed on that specific position. The modules implemented show that an evaluation of the classifiers allows sorting them so the fuzzy inference module can choose periodically the one that best suits the device context and application requirements.
Item ID: | 19814 |
---|---|
DC Identifier: | https://oa.upm.es/19814/ |
OAI Identifier: | oai:oa.upm.es:19814 |
DOI: | 10.1109/PerComW.2012.6197634 |
Official URL: | http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumbe... |
Deposited by: | Memoria Investigacion |
Deposited on: | 21 Sep 2013 07:59 |
Last Modified: | 21 Mar 2023 17:15 |