Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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Detection of Moving Direction using PIR Sensors and Deep Learning Algorithm

  • Woo, Jiyoung (Dept. of Bigdata Engineering, Soonchunhyang University) ;
  • Yun, Jaeseok (Dept. of Internet of Things, Soonchunhyang University)
  • Received : 2018.12.12
  • Accepted : 2019.02.18
  • Published : 2019.03.29
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Abstract

In this paper, we propose a method to recognize the moving direction in the indoor environment by using the sensing system equipped with passive infrared (PIR) sensors and a deep learning algorithm. A PIR sensor generates a signal that can be distinguished according to the direction of movement of the user. A sensing system with four PIR sensors deployed by $45^{\circ}$ increments is developed and installed in the ceiling of the room. The PIR sensor signals from 6 users with 10-time experiments for 8 directions were collected. We extracted the raw data sets and performed experiments varying the number of sensors fed into the deep learning algorithm. The proposed sensing system using deep learning algorithm can recognize the users' moving direction by 99.2 %. In addition, with only one PIR senor, the recognition accuracy reaches 98.4%.

Keywords

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Fig. 1. Array of 4 PIR sensors (a) and the monitored field

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Fig. 2. The PIR analog signals captured from a participant

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Fig. 3. The component layers of the proposed CNN

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Fig. 4. The framework of the proposed CNN

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Fig. 5. Deploying raw data of the PIR sensors with respect to the selected sensor sets

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Fig. 6. The PIR_1 sensor signals captured from a participant moving in eight directions

Table 1. A piece of time series data of PIR sensors

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Table 2. The selected PIR sensor sets for experiments

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Table 3. Recognition performance with respect to the selected PIR sensor sets

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