Journal of Digital Contents Society (디지털콘텐츠학회 논문지)

Digital Contents Society (한국디지털콘텐츠학회)
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Human Activity Recognition using Multi-temporal Neural Networks

다중 시구간 신경회로망을 이용한 인간 행동 인식

  • Lee, Hyun-Jin (Division of ICT Engineering, Korea Soongsil Cyber University)
  • 이현진 (숭실사이버대학교 ICT공학부)
  • Received : 2017.06.12
  • Accepted : 2017.06.25
  • Published : 2017.06.30
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Abstract

A lot of studies have been conducted to recognize the motion state or behavior of the user using the acceleration sensor built in the smartphone. In this paper, we applied the neural networks to the 3-axis acceleration information of smartphone to study human behavior. There are performance issues in applying time series data to neural networks. We proposed a multi-temporal neural networks which have trained three neural networks with different time windows for feature extraction and uses the output of these neural networks as input to the new neural network. The proposed method showed better performance than other methods like SVM, AdaBoot and IBk classifier for real acceleration data.

스마트폰에 내장된 가속도 센서를 이용하여 사용자의 동작 상태나 행동을 인식하기 위한 연구가 다양하게 진행되어 왔다. 본 논문에서는 스마트폰의 3D 가속도 정보에 신경회로망을 적용하여 사람의 행동을 인식하는 연구를 진행하였다. 시계열 데이터를 신경회로망에 그대로 적용하면 성능상의 문제가 발생한다. 따라서 여러 시구간에 대해 특징을 추출하여 각 시구간에 대해 신경회로망을 학습시키고, 이 신경회로망들의 출력들을 입력으로 하여 학습하여 구성하는 다중 시구간 신경회로망을 제안하였다. 제안하는 방법을 실제 가속도 데이터에 적용한 결과 SVM, AdaBoost, IBk 등 다른 분류기보다 우수한 성능을 보였다.

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References

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