Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (5): 1128-1135.DOI: 10.3778/j.issn.1673-9418.2010055

• Artificial Intelligence • Previous Articles     Next Articles

Detection of Health Data Based on Gaussian Mixture Generative Model

ZHU Zhuangzhuang(), ZHOU Zhiping   

  1. School of Internet of Things Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
  • Received:2020-10-20 Revised:2021-02-02 Online:2022-05-01 Published:2022-05-19
  • About author:ZHU Zhuangzhuang, born in 1995, M.S. candidate, student member of CCF. His research interests include control engineering and application.
    ZHOU Zhiping, born in 1962, Ph.D., professor. His research interests include detection technology and automation device, information security, etc.


朱壮壮(), 周治平   

  1. 江南大学 物联网工程学院,江苏 无锡 214122
  • 通讯作者: + E-mail:
  • 作者简介:朱壮壮(19995-),男,硕士研究生,CCF学生会员,主要研究方向为控制工程及应用。


Sports bracelet provides rich information for a comprehensive understanding of people’s physical health in the context of the popularity of smart wearable devices. However, some unknown outliers inevitably exist in the provided multidimensional activity data and the detection of outliers is necessary. Due to the “dimension disaster”, it is difficult to estimate the density by traditional methods, leading to poor detection performance. Aiming at the problem, a method of detecting health data is utilized, called Gaussian mixture generative model (GMGM). The model uses a variational autoencoder (VAE) to train the original data and latent features can be extracted by minimizing the reconstruction error. Then, the deep belief network (DBN) is used to predict the sample mixture membership with the help of potential distribution and the extracted features. Next, VAE, DBN and Gaussian mixture model (GMM) are optimized together to avoid the influence of model decoupling. Finally, the density of each sample point is predicted by GMM and the samples whose density is higher than the threshold in the training stage will be viewed as outliers. The performance of the GMGM is verified on the ODDS standard datasets. The results show that the model achieves a promotion of 5.5 percentage points for AUC score compared with deep autoencoding Gaussian mixture model (DAGMM). Finally, the experimental results on real datasets also show the effectiveness of GMGM.

Key words: variational autoencoder (VAE), deep brief network (DBN), Gaussian mixture model (GMM), health data, anomaly detection



关键词: 变分自编码器(VAE), 深度信念网络(DBN), 高斯混合模型(GMM), 健康数据, 异常检测

CLC Number: