基于启发式集成特征选择的人体活动识别

Human Activity Recognition Based on Heuristic Integrated Feature Selection

针对人为提取的冗余特征集和无关特征集导致可穿戴传感器的人体活动识别分类性能降低的问题,提出一种基于启发式集成特征选择的人体活动识别方法。该方法首先选取了包含功率谱密度(Power spectrum density,PSD)的特征集用于识别易混淆的活动,在此基础上借助皮尔逊系数法(Pearson correlation coefficient,PCC)筛选出低相关的特征子集,然后使用改进的正余弦优化算法(Sine cosine algorithm,SCA)进行特征优化,通过两次特征筛选得到最优特征子集。实验结果表明,在实验室采集的数据集中使用该方法后的特征子集维数为34,识别准确率达到了98.21%。在公开的SCUT-NAA数据集中进行对比实验,特征子集维数为39,低于以往基于该数据集研究方法的特征维数,并且识别准确率达到了96.51%。

To address the problem that artificially extracted redundant feature sets and irrelevant feature sets lead to the degradation of human activity recognition classification performance of wearable sensor,this paper proposes a human activity recognition method based on heuristic integrated feature selection.The method first selects the feature set containing power spectral density(PSD)for recognizing confusing activities.Then,on this basis,the method screens out the lowly correlated feature subsets with the help of Pearson correlation coefficient(PCC)method,then uses an improved sine cosine algorithm(SCA)for features and obtains the optimal feature subset by screening the feature twice.The experimental results show that the feature subset dimension after using this method in the data set collected in the laboratory is 34,and the recognition accuracy rate reaches 98.21%.In the public SCUT-NAA data set for comparison experiments,the feature subset dimension is 39,lower than the feature dimension of previous research methods,and the recognition accuracy rate reaches 96.51%.