Journal of Advanced Navigation Technology (한국항행학회논문지)

The Korean Navigation Institute (한국항행학회)
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Design and Implementation of CNN-Based Human Activity Recognition System using WiFi Signals

WiFi 신호를 활용한 CNN 기반 사람 행동 인식 시스템 설계 및 구현

  • Chung, You-shin (School of Electronics and Information Engineering, Korea Aerospace University) ;
  • Jung, Yunho (School of Electronics and Information Engineering, Korea Aerospace University)
  • 정유신 (한국항공대학교 항공전자정보공학부) ;
  • 정윤호 (한국항공대학교 항공전자정보공학부)
  • Received : 2014.09.30
  • Accepted : 2014.10.07
  • Published : 2021.08.31
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Abstract

Existing human activity recognition systems detect activities through devices such as wearable sensors and cameras. However, these methods require additional devices and costs, especially for cameras, which cause privacy issue. Using WiFi signals that are already installed can solve this problem. In this paper, we propose a CNN-based human activity recognition system using channel state information of WiFi signals, and present results of designing and implementing accelerated hardware structures. The system defined four possible behaviors during studying in indoor environments, and classified the channel state information of WiFi using convolutional neural network (CNN), showing and average accuracy of 91.86%. In addition, for acceleration, we present the results of an accelerated hardware structure design for fully connected layer with the highest computation volume on CNN classifiers. As a result of performance evaluation on FPGA device, it showed 4.28 times faster calculation time than software-based system.

기존의 사람 행동 인식 시스템은 웨어러블 센서, 카메라와 같은 장치를 통해 행동을 탐지하였다. 그러나, 이와 같은 방법들은 추가적인 장치와 비용이 요구되고, 특히 카메라 장치의 경우 사생활 침해 문제가 발생한다. 이미 설치되어 있는 WiFi 신호를 사용한다면 해당 문제를 해결할 수 있다는 장점이 있다. 본 논문에서는 WiFi 신호의 채널 상태 정보를 활용한 CNN 기반 사람 행동 인식 시스템을 제안하고, 가속 하드웨어 구조 설계 및 구현 결과를 제시한다. 해당 시스템은 실내 공간에서 학습 중 나타날 수 있는 네 가지 행동에 대해 정의하였고, 그에 대한 WiFi의 채널 상태 정보를 CNN으로 분류하여 평균 정확도는 91.86%를 보였다. 또한, 가속화를 위해 CNN 분류기에서 연산량이 가장 많은 완전 연결 계층에 대한 가속 하드웨어 구조 설계 결과를 제시하였다. FPGA 디바이스 상에서 성능 평가 결과, 기존 software 기반 시스템 대비 4.28배 빠른 연산 시간을 보임을 확인하였다.

Keywords

Acknowledgement

본 논문은 2021년도 정부 (과학기술정보통신부)의 재원으로 정보통신기획평가원의 지원을 받아 수행되었으며 (No. 2019-0-00056, 2020-0-00201), CAD tool은 IDEC에 의해 지원되었음.

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