基于三轴加速度传感器人体姿态识别的特征选择

doi:10.3969/j.issn.0255-8297.2019.03.013

Abstract

Abstract: 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.

Key words: human activity recognition, tri-axial accelerometer, feature selection, support vector machine (SVM)

CLC Number:

TP391

FAN Shurui, JIA Yating, LIU Jinghua. Feature Selection of Human Activity Recognition Based on Tri-axial Accelerometer[J]. Journal of Applied Sciences, 2019, 37(3): 427-436.

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Feature Selection of Human Activity Recognition Based on Tri-axial Accelerometer

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