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

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

Computer Science Department, Universidad Carlos III de Madrid, Avda. de la Universidad Carlos III, 22, Colmenarejo, Spain
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Received: 28 November 2008 / Revised: 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. View Full-Text
Keywords: Computer vision; Human Activity Recognition; Feature Selection; Hidden Markov Models Computer vision; Human Activity Recognition; Feature Selection; Hidden Markov Models
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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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.

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