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딥러닝 기반의 반려묘 모니터링 및 질병 진단 시스템Cat Monitoring and Disease Diagnosis System based on Deep Learnin

Other Titles
Cat Monitoring and Disease Diagnosis System based on Deep Learnin
Authors
최윤아채희찬이종욱박대희정용화
Issue Date
2021
Publisher
한국멀티미디어학회
Keywords
Cat Monitoring and Disease Diagnosis System; LSTM; GAN; Multi-Species Sensor; Veterinary Science
Citation
멀티미디어학회논문지, v.24, no.2, pp 233 - 244
Pages
12
Indexed
KCI
Journal Title
멀티미디어학회논문지
Volume
24
Number
2
Start Page
233
End Page
244
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/138741
ISSN
1229-7771
Abstract
Recently, several ICT-based cat studies have produced some successful results, according to academic and industry sources. However, research on the level of simply identifying the cat's condition, such as the behavior and sound classification of cats based on images and sound signals, has yet to be found. In this paper, based on the veterinary scientific knowledge of cats, a practical and academic cat monitoring and disease diagnosis system is proposed to monitor the health status of the cat 24 hours a day by automatically categorizing and analyzing the behavior of the cat with location information using LSTM with a beacon sensor and a raspberry pie that can be built at low cost. Validity of the proposed system is verified through experimentation with cats in actual custody (the accuracy of the cat behavior classification and location identification was 96.3% and 92.7% on average, respectively). Furthermore, a rule-based disease analysis system based on the veterinary knowledge was designed and implemented so that owners can check whether or not the cats have diseases at home (or can be used as an auxiliary tool for diagnosis by a pet veterinarian).
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