Algorithms 2009, 2(1), 282-300; doi:10.3390/a2010282
Article

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

Received: 28 November 2008; in revised form: 2 February 2009 / Accepted: 16 February 2009 / Published: 21 February 2009
(This article belongs to the Special Issue Sensor Algorithms)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: 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 the accuracy of a Hidden Markov Model generated from the subset. A comparative of the proposed techniques is presented to demonstrate their performance building Hidden Markov Models to classify different human activities using video sensors.
Keywords: Computer vision; Human Activity Recognition; Feature Selection; Hidden Markov Models
PDF Full-text Download PDF Full-Text [422 KB, uploaded 21 February 2009 13:59 CET]

Export to BibTeX |
EndNote


MDPI and ACS Style

Cilla, R.; Patricio, M.A.; García, J.; Berlanga, A.; Molina, J.M. Recognizing Human Activities from Sensors Using Hidden Markov Models Constructed by Feature Selection Techniques. Algorithms 2009, 2, 282-300.

AMA Style

Cilla R, Patricio MA, García J, Berlanga A, Molina JM. Recognizing Human Activities from Sensors Using Hidden Markov Models Constructed by Feature Selection Techniques. Algorithms. 2009; 2(1):282-300.

Chicago/Turabian Style

Cilla, Rodrigo; Patricio, Miguel A.; García, Jesús; Berlanga, Antonio; Molina, Jose M. 2009. "Recognizing Human Activities from Sensors Using Hidden Markov Models Constructed by Feature Selection Techniques." Algorithms 2, no. 1: 282-300.

Algorithms EISSN 1999-4893 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert