Abstract
In this paper, maximal relevance measure and minimal redundancy maximal relevance (mRMR) algorithm (under D-R and D/R criteria) have been applied to select features and to compose different features subsets based on observed motion sensor events for human activity recognition in smart home environments. And then, the selected features subsets have been evaluated and the activity recognition accuracy rates have been compared with two probabilistic algorithms: naïve Bayes (NB) classifier and hidden Markov model (HMM). The experimental results show that not all features are beneficial to human activity recognition and different features subsets yield different human activity recognition accuracy rates. Furthermore, even the same features subset has different effect on human activity recognition accuracy rate for different activity classifiers. It is significant for researchers performing human activity recognition to consider both relevance between features and activities and redundancy among features. Generally, both maximal relevance measure and mRMR algorithm are feasible for feature selection and positive to activity recognition.
Highlights
Besides the suitable choosing of machine learning algorithms, another key point for human activity recognition in smart home is to select valid features from sensor events datasets collected in smart home environment
Unler et al presented a hybrid filterwrapper feature subset selection algorithm based on particle swarm optimization for support vector machine classification. e filter model is based on the mutual information and is a composite measure of feature relevance and redundancy with respect to the feature subset selected [34]
MRMR feature selection algorithm applied in this paper considers the amount of information provided by these features for categorical attributes and the influence of interaction among features on classification [37]
Summary
Is example shows that the observed sensor events correspond to the Night_wandering activity with concrete Date, Time, Sensor ID, Sensor Value, and activity label parameters. Considering the actual situation, each activity has 13 features of the observed sensor events:. (1) e means of logical values of Sensor IDs of each activity’s sensor events is f1. Considering that the place where each activity happens is relatively stable, selecting the average of Sensor IDs means the focus area where the activity occurs. Considering that the place where each activity happens is relatively stable, selecting the average of Sensor IDs means the focus area where the activity occurs. e equation is ni
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