Human Activity Recognition Using Gaussian Naïve Bayes Algorithm in Smart Home

Abstract

Human activity recognition (HAR), as an important research issue, aims to identify human activities in smart homes. In this paper, we apply Gaussian Naive Bayes (GNB) algorithm to HAR and evaluate the model based on smart environment sensor data. Experimental results show that the effective selection and processing of features are helpful to improve the accuracy of activity recognition of the model. Compared with NB whose accuracy rate is 82.7%, GNB has a better accuracy rate of 89.5% and even has a higher recognition accuracy in almost every category of activities. Selecting the feature variables as good and useful as possible to get a better model in the process of activity recognition is conducive to the correct classification of samples by machine learning algorithm and improves the classification performance of the model.