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Inertial Sensor Based Human Activity Recognition via Reduced Kernel PCA

  • Donghui WuEmail author
  • Huanlong Zhang
  • Cong Niu
  • Jing Ren
  • Wanwan Zhao
Conference paper
Part of the Internet of Things book series (ITTCC)

Abstract

In the past decade, wearable inertial sensor based human activity recognition has attracted lots of attention from researchers in the world. High-dimensional feature set will increase the computation and memory cost. In this paper, kernel PCA has been utilized for dimensionality reduction to deal with inertial sensor based human activity recognition. However, kernel method may increase the computation and memory cost. Thus, reduced kernel method is proposed. The real dataset has been utilized to evaluate the proposed reduced kernel PCA (RKPCA) method. Experimental results demonstrate the efficacy of the proposed method, which achieves better results than traditional PCA method.

Keywords

Human activity recognition Inertial sensor Kernel PCA Reduced kernel method 

Notes

Acknowledgements

This work was supported by Fundamental Research in cutting-edge technologies in the project of Henan province (162300410070), Key Science Technology Program of Henan Province, China (19A413013), Foundation of Henan Educational Committee (162300410070), and Ph.D. early development program of Zhengzhou University of Light Industry (13501050009).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Donghui Wu
    • 1
    Email author
  • Huanlong Zhang
    • 2
  • Cong Niu
    • 1
  • Jing Ren
    • 1
  • Wanwan Zhao
    • 1
  1. 1.School of Building Environment EngineeringZhengzhou University of Light IndustryZhengzhouChina
  2. 2.College of Electric and Information EngineeringZhengzhou University of Light IndustryZhengzhouChina

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