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

Rodrigo Cilla* email, Miguel A. Patricioemail, Jesús Garcíaemail, Antonio Berlangaemail and Jose M. Molinaemail
Computer Science Department, Universidad Carlos III de Madrid, Avda. de la Universidad Carlos III, 22, Colmenarejo, Spain
* Author to whom correspondence should be addressed.
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)
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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

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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.

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