Accurate Fall Detection Algorithm Based on SBPSO-SVM Classifier-Reference-Cited by-同舟云学术

Accurate Fall Detection Algorithm Based on SBPSO-SVM Classifier

Published:2018 Issue: Volume: Page:
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Container-title:Proceedings of the 2018 10th International Conference on Bioinformatics and Biomedical Technology - ICBBT '18
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Author:

Xiong Weimin¹,Ning Yunkun²,Liang Shengyun²,Zhao Guoru²,Ma Yingnan³,Gao Xing³,Zhu Yuwei⁴

Affiliation:

1. Wuhan University of Sience and Technology, Wuhan, China

2. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

3. Beijing Research Center of Urban System Engineering, Beijing, China

4. Shenzhen Mingde Experimental School, Shenzhen, China

Publisher

ACM Press

Reference13 articles.

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3. Nyan. Distinguishing fall activities from normal activities by angular rate characteristics and high-speed camera characterization. Medical Engineering & Physics, 2006. pp. 842-849.

4. A. M. Khan, Y. K. Lee, S. Y. Lee, T. S. Kim, A tri-axial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer. Information Technology in Biomedicine, IEEE Transactions on, 2010. pp. 1166-1172.

5. M. Jiang, Z. L. Wang, X. B. Liu, H. Y. Zhao, Y. H. Hu. Research on Human Daily Activity recognition Method Based on BSN and CHMMs. Journal of Dalian University of Technology. 2013. vol.53. pp. 121-126.

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