Publication:
Recognizing human activities from sensors using hidden Markov models constructed by feature selection techniques

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2009-02
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MDPI Publishing
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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.
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19 pages, 8 figures.-- This article belongs to the Special Issue "Sensor Algorithms".
Keywords
Computer Vision, Human Action Recognition, Feature Selection, Hidden Markov Models
Bibliographic citation
Algorithms 2009, 2(1), p. 282-300