关键词: 运动手环, 健康数据, 异常值检测, 局部异常因子, 高斯核密度估计

Abstract: Aiming at the problem that the activity data collected by the sports bracelet have unknown abnormal data in the context of the popularity of smart wearable devices, a method for detecting abnormal data of health data based on Gaussian kernel density estimation is proposed. Firstly, the t-distributed stochastic neighbor embedding is used to extract the features of the data set and enhance the local structure of the data. Then the Gaussian kernel local density is used to replace the local reachable density in the local outlier factor algorithm. A new algorithm for calculating the outlier factor is proposed, which is called Gaussian kernel density estimation-based local outlier factor (GKDELOF) algorithm. The stability of the GKDELOF algorithm??s judgment threshold is derived and analyzed. Finally, the accuracy of the algorithm is verified by the simulation experiment on the UCI standard data set. An experimental analysis is performed on the actual activity data collected by sports bracelets. The experiment results show that this method can solve the sparse problem of health data, which is caused by complex and diverse activity, and can detect abnormal samples accurately.

Key words: sports bracelet, health data, abnormal data detection, local outlier factor, Gaussian kernel density estimation