小波分解在移动用户行为识别中的应用

针对目前行为识别通用模型对步行、上楼、下楼等易混淆行为识别准确率较低的情况，提出了一种基于小波分解的移动用户行为识别方法，从小波分解后不同频率子信号的低频近似系数中提取小波能量、小波峰个数和平均波峰幅值等特征，基于决策树分类器建立与用户无关的行为识别通用模型. 分别用典型时域特征数据集和小波特征数据集对该通用模型进行验证. 实验结果表明，采用新方法后，3种易混淆行为的平均识别准确率提高了14.82%，减少了误判.

Application of Wavelet Decomposition in Mobile User Behavior Recognition

For case of low recognition accuracy when using universal model to distiguish confusing human behaviors such as walking, going upstairs and downstairs, a mobile user behavior recognition method based on wavelet decomposition was proposed. It extracts the wavelet energy distribution, the number of wavelet peak and the everage wavelet peak amplitude from the sub-signals generated by wavelet decompo-sition, and also the decision tree classifier is used to build the user-independent behavior recognition model. The typical time-domain feature dataset and wavelet feature dataset were respectively used to train and test the universal model. Experiments show that the proposed method improves the average accuracy about 14. 82% of the three confusing behaviors, and reduces the possibility of misjudgment.