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Deep Learning-based Pet Monitoring System and Activity Recognition device

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2022, 27(2), pp.25-32
  • DOI : 10.9708/jksci.2022.27.02.025
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : January 7, 2022
  • Accepted : February 3, 2022
  • Published : February 28, 2022

Kim JinAh 1 Hyungju Kim 1 Chan Park 1 Nammee Moon 2

1호서대학교
2호서대학교 컴퓨터정보공학부

Accredited

ABSTRACT

In this paper, we propose a pet monitoring system based on deep learning using an activity recognition device. The system consists of a pet's activity recognition device, a pet owner's smart device, and a server. Accelerometer and gyroscope data were collected from an Arduino-based activity recognition device, and the number of steps was calculated. The collected data is pre-processed and the amount of activity is measured by recognizing the activity in five types (sitting, standing, lying, walking, running) through a deep learning model that hybridizes CNN and LSTM. Finally, monitoring of changes in the activity, such as daily and weekly briefing charts, is provided on the pet owner's smart device. As a result of the performance evaluation, it was confirmed that specific activity recognition and activity measurement of pets were possible. Abnormal behavior detection of pets and expansion of health care services can be expected through data accumulation in the future.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.