Recognizing Human Activities from Sensors Using Hidden Markov Models Constructed by Feature Selection Techniques
Open Access
- 21 February 2009
- journal article
- research article
- Published by MDPI AG in Algorithms
- Vol. 2 (1) , 282-300
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
This publication has 15 references indexed in Scilit:
- Intelligent environment for monitoring Alzheimer patients, agent technology for health careDecision Support Systems, 2008
- Development of intelligent multisensor surveillance systems with agentsRobotics and Autonomous Systems, 2007
- Comparison Between Genetic Algorithms and the Baum-Welch Algorithm in Learning HMMs for Human Activity Classification2007
- Comparison of Classifiers for Human Activity Recognition2007
- Object trackingACM Computing Surveys, 2006
- A general method for human activity recognition in videoComputer Vision and Image Understanding, 2006
- Toward integrating feature selection algorithms for classification and clusteringIEEE Transactions on Knowledge and Data Engineering, 2005
- Discovery and segmentation of activities in videoIEEE Transactions on Pattern Analysis and Machine Intelligence, 2000
- A Bayesian computer vision system for modeling human interactionsIEEE Transactions on Pattern Analysis and Machine Intelligence, 2000
- Visual pattern recognition by moment invariantsIRE Transactions on Information Theory, 1962