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An Online Adaptive Sampling Rate Learning Framework for Sensor-Based Human Activity Recognition

  • Zeyi Jin
  • Jingjing CaoEmail author
  • Jingtao Sun
  • Wenfeng Li
  • Qiang Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11226)

Abstract

In the field of sensor based human activity recognition, fixed sampling rate scheme is difficult to accommodate the dynamic characteristics of streaming data. It may directly leads to high energy consumption or activities detail missing problems. In this paper, an efficiency online activity recognition framework is proposed by integrating sampling rate optimization with novel class detection and recurring class detection algorithms. Based on the proposed framework, we believe that this system can effectively save battery life and computation capacity without decreasing the overall recognition performance.

Keywords

Human activity recognition Sampling rate optimization Novel class detection 

Notes

Acknowledgment

The work was partially supported by National Natural Science Foundation of China under the Grant No. 61502360, No. 61571336 and No. 61503291.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Zeyi Jin
    • 1
  • Jingjing Cao
    • 1
    Email author
  • Jingtao Sun
    • 2
  • Wenfeng Li
    • 1
  • Qiang Wang
    • 1
  1. 1.School of Logistics EngineeringWuhan University of TechnologyWuhanChina
  2. 2.Information Systems Architecture Research DivisionNational Institute of InformaticsTokyoJapan

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