Recognizing Human Activities from Sensors Using Hidden Markov Models Constructed by Feature Selection Techniques

Recognizing Human Activities from Sensors Using Hidden Markov Models Constructed by Feature Selection Techniques

In this paper a method for selecting features for Human Activity Recognition from sensors is presented. Using a large feature set that contains features that may describe the activities to recognize, Best First Search and Genetic Algorithms are employed to select the feature subset that maximizes th...

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Bibliographic Details
Main Authors: Rodrigo Cilla, Miguel A. Patricio, Jesús García, Antonio Berlanga, Jose M. Molina
Format: Article
Language:English
Published: MDPI AG 2009-02-01
Series:Algorithms
Subjects:
Computer vision
Human Activity Recognition
Feature Selection
Hidden Markov Models
Online Access:http://www.mdpi.com/1999-4893/2/1/282/

Internet

http://www.mdpi.com/1999-4893/2/1/282/

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