An improved hidden markov model based on weighted observation

被引：0

作者：

Wang C. ^{[1
]}

Li Z. ^{[2
]}

Wang B. ^{[1
]}

Xu Y. ^{[3
]}

Huang W. ^{[1
]}

机构：

[1] Software Engineering College, Zhengzhou University of Light Industry, Zhengzhou

[2] Henan Provincial Institute of Scientific and Technical Information, Zhengzhou

[3] School of Cyber Science and Engineering, Southeast University, Nanjing

来源：

Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2019年 / 32卷 / 06期

基金：

中国国家自然科学基金;

关键词：

Baum-Welch Algorithm; Ctivity Recognition; Hidden Markov Model; Sequence Labeling;

D O I：

10.16451/j.cnki.issn1003-6059.201906004

中图分类号：

学科分类号：

摘要：

As the classic hidden Markov model(HMM) loses the sight of confidence of labeled results while building a sequence, a weighted observation hidden Markov model(WOHMM) is proposed. The algorithms in the steps of probability calculation, parameter learning as well as sequence labeling are described in detail. The simulation results on the public datasets show that the parameters obtained by the parameter learning algorithm of WOHMM are closer to the real values than those of HMM, and the performance of sequence labeling algorithm is superior to the state-of-the-art methods. © 2019, Science Press. All right reserved.

引用

页码：515 / 523

页数：8

共 21 条

[1]

Bulling A., Blanke U., Schiele B., A Tutorial on Human Activity Recognition Using Body-Worn Inertial Sensors, ACM Computing Surveys, 46, 3, (2014)

[2]

Scheurer S., Tedesco S., Brown K.N., Et al., Human Activity Recognition for Emergency First Responders via Body-Worn Inertial Sensors, Proc of the 14th IEEE International Conference on Wearable and Implantable Body Sensor Networks, pp. 5-8, (2017)

[3]

Sanchez V.G., Pfeiffer C.F., Skeie N.O., A Review of Smart House Analysis Methods for Assisting Older People Living Alone, Journal of Sensor and Actuator Networks, 6, 3, (2017)

[4]

Yang Z., Wu C.S., Zhou Z., Et al., Mobility Increases Localizability: A Survey on Wireless Indoor Localization Using Inertial Sensors, ACM Computing Surveys, 47, 3, (2015)

[5]

Hammerla N.Y., Ploetz T., Let's (not) Stick Together: Pairwise Similarity Biases Cross-Validation in Activity Recognition, Proc of the ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 1041-1051, (2015)

[6]

Wang Z.L., Wu D.H., Gravina R., Et al., Kernel Fusion Based Extreme Learning Machine for Cross-Location Activity Recognition, Information Fusion, 37, pp. 1-9, (2017)

[7]

Hoseini-Tabatabaei S.A., Gluhak A., Tafazolli R., A Survey on Smartphone-Based Systems for Opportunistic User Context Recognition, ACM Computing Surveys, 45, 3, (2013)

[8]

Phan T., Improving Activity Recognition via Automatic Decision Tree Pruning, Proc of the ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 827-832, (2014)

[9]

Krishnan N.C., Panchanathan S., Analysis of Low Resolution Accelerometer Data for Continuous Human Activity Recognition, Proc of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3337-3340, (2008)

[10]

Zheng L.X., Wu D.H., Ruan X.Y., Et al., A Novel Energy-Efficient Approach for Human Activity Recognition, Sensors, 17, 9, (2017)

← 1 2 3 →