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http://dx.doi.org/10.9717/kmms.2020.24.2.233

Cat Monitoring and Disease Diagnosis System based on Deep Learning  

Choi, Yoona (Dept of Computer and Information Science, Korea University)
Chae, Heechan (InfoValleyKorea)
Lee, Jonguk (Dept of Computer and Convergence Software, Korea University)
Park, Daihee (Dept of Computer and Convergence Software, Korea University)
Chung, Yongwha (Dept of Computer and Convergence Software, Korea University)
Publication Information
Journal of Korea Multimedia Society / v.24, no.2, 2021 , pp. 233-244 More about this Journal
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).
Keywords
Cat Monitoring and Disease Diagnosis System; LSTM; GAN; Multi-Species Sensor; Veterinary Science;
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