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http://dx.doi.org/10.9708/jksci.2022.27.02.025

Deep Learning-based Pet Monitoring System and Activity Recognition device  

Kim, Jinah (Dept. of Computer Science and Engineering, Hoseo University)
Kim, Hyungju (Dept. of Computer Science and Engineering, Hoseo University)
Park, Chan (Dept. of Computer Science and Engineering, Hoseo University)
Moon, Nammee (Dept. of Computer Science and Engineering, Hoseo University)
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.
Keywords
Monitoring system; Activity recognition device; Deep learning; Activity recognition; Activity analysis;
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1 Y. Chen, and M. Elshakankiri, "Implementation of an IoT based pet care system," In 2020 Fifth International Conference on Fog and Mobile Edge Computing, pp. 256-262, Paris, France, April 2020. DOI: 10.1109/FMEC49853.2020.9144910   DOI
2 R. C. Chen, V. S. Saravanarajan, and H. T. Hung, "Monitoring the behaviours of pet cat based on YOLO model and raspberry Pi," International Journal of Applied Science and Engineering, Vol. 18, No. 5, pp. 1-12, September 2021. DOI: 10.6703/IJASE.202109_18(5).016   DOI
3 M. Foster, S. Mealin, M. Gruen, D. L. Roberts, and A. Bozkurt, "Preliminary Evaluation of a Wearable Sensor System for Assessment of Heart Rate, Heart Rate Variability, and Activity Level in Working Dogs," In 2019 IEEE SENSORS, Montreal, Canada, pp. 1-4, October 2019. DOI: 10.1109/SENSORS43011.2019.8956771   DOI
4 S. Aich, S. Chakraborty, J. Sim, D. Jang, and H. Kim, "The design of an automated system for the analysis of the activity and emotional patterns of dogs with wearable sensors using machine learning," Applied Sciences, Vol. 9, No. 22, pp. 1-22, November 2019. DOI: 10.3390/app9224938   DOI
5 C. Ladha, J. O'Sullivan, Z. Belshaw, and L. Asher, "Gaitkeeper: A system for measuring canine gait," Sensors, Vol. 17, No. 2, pp. 1-17, February 2017. DOI: 10.3390/s17020309   DOI
6 K. Czajkowski, S. Fitzgerald, I. Foster, and C. Kesselman, "Grid Information Services for Distributed Resource Sharing," In 10th IEEE International Symposium on High Performance Distributed Computing, pp. 181-184, San Francisco, United States, August 2001. DOI: 10.1109/HPDC.2001.945188   DOI
7 C. C. Aggarwal, "Neural Networks and Deep Learning," Springer, pp. 38-42, 2018
8 R. D. Chambers, N. C. Yoder, A. B. Carson, C. Junge, D. E. Allen, L. M. Prescott, S. Bradley, G. Wymore, K. Lloyd, and S. Lyle, "Deep learning classification of canine behavior using a single collar-mounted accelerometer: Real-world validation," Animals, Vol. 11, No. 6, pp. 1-19, May 2021. DOI: 10.3390/ani11061549   DOI
9 J. Colpoys, and D. DeCock, "Evaluation of the FitBark Activity Monitor for Measuring Physical Activity in Dogs," Animals, Vol. 11, No. 3, pp. 1-8, March 2021. DOI: 10.3390/ani11030781   DOI
10 A. Ferrari, D. Micucci, M. Mobilio, and P. Napoletano, "Human activities recognition using accelerometer and gyroscope," In European Conference on Ambient Intelligence, pp. 357-362, Rome, Italy, November 2019. DOI: 10.1007/978-3-030-34255-5_28   DOI
11 H. Kim and N. Moon "1D-CNN-LSTM based Pet Behavior Recognition using Wearable device," The 13th International Conference on Computer Science and its Applications, pp. 1-6, Jeju, Korea, December 2021.
12 D. Van Der Linden, A. Zamansky, I. Hadar, B. Craggs, and A. Rashid, "Buddy's wearable is not your buddy: Privacy implications of pet wearables," IEEE Security & Privacy, Vol. 17, No. 3, pp. 28-39, May 2019. DOI: 10.1109/MSEC.2018.2888783   DOI
13 Github, SparkFun LSM6DS3 Arduino Library, https://github.com/sparkfun/SparkFun_LSM6DS3_Arduino_Library
14 W. J. M. Boteju, H. M. K. S. Herath, M. D. P. Peiris, A. K. P. E. Wathsala, P. Samarasinghe, and L. Weerasinghe, "Deep Learning Based Dog Behavioural Monitoring System," In 2020 3rd International Conference on Intelligent Sustainable Systems, pp. 82-87, Thoothukudi, India, December 2020. DOI: 10.1109/ICISS49785.2020.9315983   DOI
15 H. Wang, J. Zhao, J. Li, L. Tian, P. Tu, T. Cao, Y. An, K. Wang, S. Li, "Wearable sensor-based human activity recognition using hybrid deep learning techniques," Security and Communication Networks, Vol. 2020, pp. 1-12, July 2020. DOI: 10.1155/2020/2132138   DOI
16 D. Setiawan, M. W. Sari, and R. H. Hardyanto, "Geofencing technology implementation for pet tracker using Arduino based on Android," In Journal of Physics: Conference Series, Vol. 1823, No. 1, pp. 1-10, November 2020. DOI: 10.1088/1742-6596/1823/1/012055   DOI
17 KDB Bank Future Strategy Research Center, "Weekly KDB report," Vol. 936, https://rd.kdb.co.kr/fileView?groupId=E2575B12-A76C-DE36-BB0A-135AE6F5EC56&fileId=8B322F1B-255A-6A0D-1C11-CA1C6B7D7CC9
18 KB Financial Group Management Research Center, "2021 Korea Pet Report," https://www.kbfg.com/kbresearch/report/reportView.do?reportId=2000160
19 P. Paci, C. Mancini, and B. A. Price, "Wearer-centered design for animal biotelemetry: implementation and wearability test of a prototype," In Proceedings of the 23rd International Symposium on Wearable Computers, pp. 177-185, New York, United States, September 2019. DOI: 10.1145/3341163.3347750   DOI
20 P. Paci, C. Mancini, and B. A. Price, "Understanding the Interaction Between Animals and Wearables: The Wearer Experience of Cats," In Proceedings of the 2020 ACM Designing Interactive Systems Conference, pp. 1701-1712, New York, United States, July 2020. DOI: 10.1145/3357236.3395546   DOI
21 Statistics Korea, "Results of the 2020 Population and Housing Census," http://kostat.go.kr/assist/synap/preview/skin/miri.html?fn=e5e1074842287827113312&rs=/assist/synap/preview
22 P. Kumpulainen, A. V. Cardo, S. Somppi, H. Tornqvist, H. Vaataja, P. Majaranta, Y, Gizatdinova, C. H. Antink, V. Surakka, M. V. Kujala, O. Vainio, and A. Vehkaoja, "Dog behaviour classification with movement sensors placed on the harness and the collar," Applied Animal Behaviour Science, Vol. 241, No. 105393, pp 1-7, August 2021. DOI: 10.1016/j.applanim.2021.105393   DOI