Cat Body Language Recognition Using Computer Vision in an Android Application
Abstract
Understanding cat behaviour is essential for fostering healthy human-cat relationships, but its inherent complexity frequently leads to misunderstandings. This study introduces Emeowtions, an innovative Android application employing artificial intelligence (AI) to decipher cat emotions and body language in real-time. Addressing market gaps for comprehensive tools, Emeowtions integrates the YOLOv8n object detection model with a custom-trained multi-label classification model for cat emotion and body language analysis. The custom model was developed based on the CRoss Industry Standard for Data Mining (CRISP-DM) framework and trained using transfer learning with MobileNetV3 on a custom curated dataset of annotated cat images. Built using the Waterfall methodology, the application allows users to obtain real-time, AI-driven insights via their smartphone camera. Beyond that, it provides a hybrid recommendation system suggesting tailored behaviour suggestions, a user feedback loop for model refinement, and a direct chat interface for veterinary consultations. Technical evaluation showed the AI model achieved a recall of 0.742. Overall, Emeowtions offers a valuable, practical tool that demonstrates AI's capability to reduce misinterpretations of cat behaviour, ultimately fostering healthier human-animal relationships and contributing to improved cat welfare.
Full Text:
PDFReferences
Alamanda, D. T., Wibowo, L. A., Munawar, S., & Nisa, A. K. (2021). The interest of technology adoption in e-commerce mobile apps using modified unified theory of acceptance and use of technology 2 in Indonesia. International Journal of Applied Business and International Management, 6(3), Article 3. https://doi.org/10.32535/ijabim.v6i3.1327
Atlassian. (2025). Waterfall methodology: A comprehensive guide. Atlassian. https://www.atlassian.com/agile/project-management/waterfall-methodology
Backlinko. (2024, March 13). iPhone vs Android statistics. Backlinko. https://backlinko.com/iphone-vs-android-statistics
Chen, H.-Y., Lin, C.-H., Lai, J.-W., & Chan, Y.-K. (2023). Convolutional neural network-based automated system for dog tracking and emotion recognition in video surveillance. Applied Sciences, 13(7). https://doi.org/10.3390/app13074596
Dai, Y., Liu, Y., & Zhang, S. (2021). Mask R-CNN-based cat class recognition and segmentation. Journal of Physics: Conference Series, 1966(1). https://doi.org/10.1088/1742-6596/1966/1/012010
Eagan, B. H., Eagan, B., & Protopopova, A. (2022). Behaviour real-time spatial tracking identification (BeRSTID) used for cat behaviour monitoring in an animal shelter. Scientific Reports, 12. https://doi.org/10.1038/s41598-022-22167-3
Egerton, F. N. (2016). History of ecological sciences, part 56: Ethology until 1973. The Bulletin of the Ecological Society of America, 97(1), 31–88. https://doi.org/10.1002/bes2.1219
Fernandes, A. F. A., Dorea, J. R. R. D., & Rosa, G. J. de M. (2020). Image analysis and computer vision applications in animal sciences: An overview. Frontiers in Veterinary Science, 7. https://doi.org/10.3389/fvets.2020.551269
Ferres, K., Schloesser, T., & Gloor, P. A. (2022). Predicting dog emotions based on posture analysis using DeepLabCut. Future Internet, 14(4), 97. https://doi.org/10.3390/fi14040097
Gerken, A. (2023, November 27). How to read your cat’s tail language. petMD. https://www.petmd.com/cat/behavior/cat-tail-language
Holton, M. D., Wilson, R. P., Teilmann, J., & Siebert, U. (2021). Animal tag technology keeps coming of age: An engineering perspective. Philosophical Transactions of the Royal Society, 376(1831). https://doi.org/10.1098/rstb.2020.0229
Horwitz, D., & Houpt, K. A. (2020). Progress in veterinary behavior in North America: The case of the American College of Veterinary Behaviorists. Animals, 10(3). https://doi.org/10.3390/ani10030536
Hotz, N. (2024, December 9). What is CRISP DM? Data Science PM. https://www.datascience-pm.com/crisp-dm-2/
Howard, A., Sandler, M., Chen, B., Wang, W., Chen, L.-C., Tan, M., Chu, G., Vasudevan, V., Zhu, Y., Pang, R., Adam, H., & Le, Q. (2019). Searching for MobileNetV3. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (pp. 1314–1324). https://doi.org/10.1109/ICCV.2019.00140
InternationalCatCare. (2018). Cat communication. International Cat Care. https://icatcare.org/advice/cat-communication
Jajodia, T., & Garg, P. (2019). Image classification – Cat and dog images. International Research Journal of Engineering and Technology, 6(12). https://www.irjet.net/archives/V6/i12/IRJET-V6I1271.pdf
Jang, H., Ham, E., & Ahn, S. (2021). CatEmotion. GitHub. https://github.com/hammii/CatEmotion/wiki
Ko, H., Lee, S., Park, Y., & Choi, A. (2022). A survey of recommendation systems: Recommendation models, techniques, and application fields. Electronics, 11(1). https://doi.org/10.3390/electronics11010141
Liem, G. S., Koay, L. K., Sanderan, P. A., Pong, H. L., Poon, Z. Y., Marimuthu, S. A., Gisca, A. P., Gupta, M., Saxena, M., & Kee, D. M. H. (2023). AI-assisted food ordering and delivery management system for KFC: Insights from Malaysia, Indonesia and India. Journal of The Community Development in Asia, 6(3), Article 3. https://doi.org/10.32535/jcda.v6i3.2540
Lin, T.-Y., Maire, M., Belongie, S., Bourdev, L., Girshick, R., Hays, J., Perona, P., Ramanan, D., Zitnick, C. L., & Dollar, P. (2015). Microsoft COCO: Common objects in context. Computer Vision and Pattern Recognition. [Conference paper; URL missing DOI or link.]
Monteiro, B. P., Lee, N. H., & Steagall, P. V. (2023). Can cat caregivers reliably assess acute pain in cats using the Feline Grimace Scale? A large bilingual global survey. Journal of Feline Medicine and Surgery, 25(1). https://doi.org/10.1177/1098612X221145499
Powell, L., Watson, B., & Serpell, J. (2023). Understanding feline feelings: An investigation of cat owners’ perceptions of problematic cat behaviors. Applied Animal Behaviour Science, 266. https://doi.org/10.1016/j.applanim.2023.106025
Ritika, B. (2024, April 3). What are the top 6 trends shaping the future of pet care market? LinkedIn. https://www.linkedin.com/pulse/what-top-6-trends-shaping-future-pet-care-market-ritika-b-z1lbc/
Santovito, A., Buglisi, M., Sciandra, C., & Scarfo, M. (2022). Buccal micronucleus assay as a useful tool to evaluate the stress-associated genomic damage in shelter dogs and cats: New perspective in animal welfare. Journal of Veterinary Behaviour, 47, 22–28. https://doi.org/10.1016/j.jveb.2021.09.007
Sharma, A. (2024). The definitive guide to cat behavior and body language [Guide]. Tuft and Paw. https://www.tuftandpaw.com/blogs/cat-guides/the-definitive-guide-to-cat-behavior-and-body-language
Shouran, Z., & Ali, D. A. (2024). The implementation of artificial intelligence in human resources management. Journal of International Conference Proceedings, 7(1), Article 1. https://doi.org/10.32535/jicp.v7i1.2993
Stanton, L. A., Sullivan, M. S., & Fazio, J. M. (2015). A standardized ethogram for the felidae: A tool for behavioral researchers. Applied Animal Behaviour Science, 173, 3–16. https://doi.org/10.1016/j.applanim.2015.04.001
Steagall Laboratory. (2025, January 20). Feline Grimace Scale [App Store]. Google Play. https://play.google.com/store/apps/details?id=com.universitedemontreal.felinegrimacescale&hl=en
Sylvester. (2022). Tably for cat parents. Sylvester.Ai. https://www.sylvester.ai/cat-owners
Tavernier, C., Ahmed, S., Houpt, K. A., & Yeon, S. C. (2020). Feline vocal communication. Journal of Veterinary Science, 21(1). https://doi.org/10.4142/jvs.2020.21.e18
Ullmann, W., Fischer, C., Kramer-Schadt, S., Walzl, K. P., Eccard, J. A., Wevers, J. P., Hardert, A., Sliwinski, K., Crawford, M. S., Glemnitz, M., & Blaum, N. (2023). The secret life of wild animals revealed by accelerometer data: How landscape diversity and seasonality influence the behavioural types of European hares. Landscape Ethology, 38, 3081–3095. https://doi.org/10.1007/s10980-023-01765-0
World Health Organization. (2024). Animal bites. https://www.who.int/news-room/fact-sheets/detail/animal-bites
Xu, M. (2024). Analysis of cat’s communication style and cognitive ability. International Journal of Molecular Zoology, 14(1), 1–8. https://doi.org/10.5376/ijmz.2024.14.0001
Zhao, Z. (2020). Understanding cat behavior: Using notational systems to represent the relationship of cats’ postures and facial expression [Master’s thesis, Northeastern University]. https://repository.library.northeastern.edu/files/neu:bz60mb444
DOI: https://doi.org/10.32535/jicp.v8i1.3999
Refbacks
- There are currently no refbacks.
Copyright (c) 2025 Wen Zheng Thor, Vasuky Mohanan
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Published by:
AIBPM Publisher
Editorial Office:
JL. Kahuripan No. 9 Hotel Sahid Montana, Malang, Indonesia
Phone:+62 341 366222
Email: journal.jicp@gmail.com
Website:http://ejournal.aibpmjournals.com/index.php/JICP
Supported by: Association of International Business & Professional Management
If you are interested to get the journal subscription you can contact us at admin@aibpm.org.
ISSN 2622-0989 (Print)
ISSN 2621-993X (Online)
DOI:Prefix 10.32535 by CrossREF
Journal of International Conference Proceedings (JICP) INDEXED:
In Process
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.