In order to solve the problem of activity classification in human motion pattern recognition, the feature selection of human activity recognition is studied in detail. By taking signal statistics on the x-axis, y-axis and z-axis, 117 features such as standard deviation, skewness and kurtosis are obtained. The three algorithms of Fisher score, Relief-F and Chi square are combined with the hierarchical classification method to select the feature subset, and the action classification is conducted by using the support vector machine (SVM). Experiments showed that the feature set selected by the three feature selection algorithms helps to identify three static movements of standing, sitting and lying and three dynamic movements of walking, going upstairs and downstairs with high precision, which is conducive to the subsequent research on low complexity online identification method.
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