Daily Activity Feature Selection in Smart Homes Based on Pearson Correlation Coefficient
Daily Activity Feature Selection in Smart Homes Based on Pearson Correlation Coefficient
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DOI：
10.1007/s11063-019-10185-8
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发表时间：
2020-04-01
影响因子：
3.1
通讯作者：
Guo, Jinghuan
中科院分区：
文献类型：
--
作者：
Liu, Yaqing;Mu, Yong;Guo, Jinghuan
就智能家居而言，识别日常活动的能力主要取决于用于选择与这些活动相关的适当功能的策略。为了实现这一目标，本文提出了一种基于皮尔逊相关系数的日常活动特征选择策略。首先，将日常活动特征视为皮尔逊相关系数公式中的向量。其次，根据加权皮尔逊相关系数公式得到日常活动特征之间的关联度。最后根据日常活动特征之间的关联度去除冗余特征。采用两个不同的数据集来减轻所使用的数据集和传感器配置的耦合的影响。采用三种不同的机器学习技术来评估所提出的方法在活动识别中的性能。实验结果表明，所提出的方法具有更高的识别率，平均改进 F 值分别为 1.56% 和 2.7%。
In the case of a smart home, the ability to recognize daily activities depends primarily on the strategy used for selecting the appropriate features related to these activities. To achieve the goal, this paper presents a daily activity feature selection strategy based on the Pearson Correlation Coefficient. Firstly, a daily activity feature is viewed as a vector in Pearson Correlation Coefficient formula. Secondly, the relation degree between daily activity features is obtained according to weighted Pearson Correlation Coefficient formula. At last, redundant features are removed by the relation degree between daily activity features. Two distinct datasets are adopted to mitigate the effects of the coupling of the dataset used and the sensor configuration. Three different machine learning techniques are employed to evaluate the performance of the proposed approach in activity recognition. The experiment results show that the proposed approach yields higher recognition rates and achieves average improvement F-measures of 1.56% and 2.7%, respectively.