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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 1
103-105,114

Research on feature vector extraction and dimension reduction of wearable fall detection

Li Lei
Zhang Fan
Shi Huaji
Zhou Conghua
School of Computer Science & Telecommunication Engineering, Jiangsu University, Zhenjiang Jiangsu 212013, China
DOI: 10.19734/j.issn.1001-3695.2017.07.0674

Abstract

In wearable fall detection of the elderly, too much characteristics will cause the curse of dimensionality, and affect the accuracy of subsequent fall detection. To solve this problem, this paper used time domain analysis method to extract feature vector. The proposed improved kernel principal component analysised(IKPCA) algorithm was used to reduce the feature vectors, so as to obtain high-quality feature vectors, which made the subsequent classification more effective. IKPCA algorithm firstly used the I-RELIEF algorithm to select the initial feature vectors, then calculated the information measure and similarity measure of the falling feature vectors. Finally, according to the similarity measurement of the falling feature vectors, it eliminated the invalid falling feature vectors. The IKPCA algorithm can not only keep better dimensionality reduction ability of the KPCA algorithm, but also expands better classification ability. Experiments ran on real data sets. The comparative analysis shows that, compared with other algorithms, the IKPCA algorithm can obtain higher-quality feature vector data set.

Foundation Support

江苏省六大人才高峰项目(2014-WLW-012)
江苏省重点研发计划(社会发展)资助项目(BE2016630,BE2015617)
无锡市卫计委重点项目(Z201603)
无锡市科技型中小企业创新基金资助项目(WX0301-B010508-160104-PB)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2017.07.0674
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 1
Section: Algorithm Research & Explore
Pages: 103-105,114
Serial Number: 1001-3695(2019)01-023-0103-03

Publish History

[2019-01-05] Printed Article

Cite This Article

李雷, 张帆, 施化吉, 等. 穿戴式跌倒检测中特征向量的提取和降维研究 [J]. 计算机应用研究, 2019, 36 (1): 103-105,114. (Li Lei, Zhang Fan, Shi Huaji, et al. Research on feature vector extraction and dimension reduction of wearable fall detection [J]. Application Research of Computers, 2019, 36 (1): 103-105,114. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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