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Activity recognition using back-propagation algorithm and minimum redundancy feature selection method | IEEE Conference Publication | IEEE Xplore

Activity recognition using back-propagation algorithm and minimum redundancy feature selection method


Abstract:

In this paper, we use multilayer Perceptron model and a supervised learning technique called backpropagation to train a neural network in order to recognize human activit...Show More

Abstract:

In this paper, we use multilayer Perceptron model and a supervised learning technique called backpropagation to train a neural network in order to recognize human activity inside smart home, and select useful features according to minimum redundancy maximum relevance. The results show that different feature datasets and different number of neurons of hidden layer of neural network yield different activity recognition accuracy. The selection of suitable feature datasets increases the activity recognition accuracy and reduces the time of execution. Furthermore, neural network using back-propagation algorithm and multilayer Perceptron model has relatively better human activity recognition performances.
Date of Conference: 24-26 October 2016
Date Added to IEEE Xplore: 05 January 2017
ISBN Information:
Electronic ISSN: 2327-1884
Conference Location: Tangier, Morocco

References

References is not available for this document.