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Human activity recognition based on feature selection in smart home using back-propagation algorithm
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DOI：10.1016/j.isatra.2014.06.008.png)
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Abstract
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In this paper, Back-propagation(BP) algorithm has been used to train the feed forward neural network for human activity recognition in smart home environments, and inter-class distance method for feature selection of observed motion sensor events is discussed and tested. And then, the human activity recognition performances of neural network using BP algorithm have been evaluated and compared with other probabilistic algorithms: Naive Bayes(NB) classifier and Hidden Markov Model(HMM). The results show that different feature datasets yield different activity recognition accuracy. The selection of unsuitable feature datasets increases the computational complexity and degrades the activity recognition accuracy. Furthermore, neural network using BP algorithm has relatively better human activity recognition performances than NB classifier and HMM. (C) 2014 ISA. Published by Elsevier Ltd. All rights reserved.
Keywords:
Human activity recognition
Sensors and networks
Pervasive computing
Feature selection
Smart home
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