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

摘要

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.