The Transactions of The Korean Institute of Electrical Engineers (전기학회논문지)

The Korean Institute of Electrical Engineers (대한전기학회)
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The Analysis of Living Daily Activities by Interpreting Bi-Directional Accelerometer Signals with Extreme Learning Machine

2축 가속도 신호와 Extreme Learning Machine을 사용한 행동패턴 분석 알고리즘

  • 신항식 (연세대학교 전기전자공학과) ;
  • 이영범 (연세대학교 전기전자공학과) ;
  • 이명호 (연세대학교 공대 전기전자공학과)
  • Published : 2007.07.01
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Abstract

In this paper, we propose pattern recognition algorithm for activities of daily living by adopting extreme learning machine based on single layer feedforward networks(SLFNs) to the signal from bidirectional accelerometer. For activity classification, 20 persons are participated and we acquire 6, types of signals at standing, walking, running, sitting, lying, and falling. Then, we design input vector using reduced model for ELM input. In ELM classification results, we can find accuracy change by increasing the number of hidden neurons. As a result, we find the accuracy is increased by increasing the number of hidden neuron. ELM is able to classify more than 80 % accuracy for experimental data set when the number of hidden is more than 20.

Keywords

References

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