딥러닝 기반 반려동물 모니터링 시스템 및 활동 인식 장치
Deep Learning-based Pet Monitoring System and Activity Recognition device
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
본 논문에서는 활동 인식장치를 이용한 딥러닝 기반의 반려동물 모니터링 시스템을 제안한다. 이 시스템은 반려동물의 활동 인식장치와 반려인의 스마트 기기, 서버로 구성된다. 아두이노 기반활동 인식 장치로부터 가속도와 자이로 데이터를 수집하고, 이로부터 반려동물의 걸음 수를 연산하였다. 수집된 데이터는 전처리 과정을 거쳐 CNN과 LSTM을 하이브리드한 딥러닝 모델을 통해5가지 형태(앉기, 서기, 눕기, 걷기, 뛰기)로 활동을 인식함으로써 활동량을 측정한다. 마지막으로, 반려인의 스마트 기기에 일일 및 주간 브리핑 차트 등 활동 변화에 대한 모니터링을 제공한다. 성능 평가 결과, 반려동물의 구체화된 활동 인식 및 활동량 측정이 가능함을 확인하였다. 향후 데이터 축적을 통해 반려동물의 이상행동 탐지 및 헬스 케어 서비스의 확장을 기대할 수 있다.
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.
참고문헌 (22)
- "Results of the 2020 Population and Housing Census"[URL]
- "Weekly KDB report, Vol. 936"[URL]
- "2021 Korea Pet Report"[URL]
- P. Paci, "Wearer-centered design for animal biotelemetry: implementation and wearability test of a prototype", Proceedings of the 23rd International Symposium on Wearable Computers, pp.177~185 (2019)
- Y. Chen, "Implementation of an IoT based pet care system", 2020 Fifth International Conference on Fog and Mobile Edge Computing, pp.256~262 (2020)
- R. C. Chen, "Monitoring the behaviours of pet cat based on YOLO model and raspberry Pi", International Journal of Applied Science and Engineering, 18(5), pp.1~12 (2021)[DOI]
- P. Kumpulainen, "Dog behaviour classification with movement sensors placed on the harness and the collar", Applied Animal Behaviour Science, 241(105393), pp.1~7 (2021)[DOI]
- M. Foster, "Preliminary Evaluation of a Wearable Sensor System for Assessment of Heart Rate, Heart Rate Variability, and Activity Level in Working Dogs", 2019 IEEE SENSORS, pp.1~4 (2019)
- S. Aich, "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, 9(22), pp.1~22 (2019)[DOI]
- C. Ladha, "Gaitkeeper:A system for measuring canine gait", Sensors, 17(2), pp.1~17 (2017)[DOI]
- K. Czajkowski, "Grid Information Services for Distributed Resource Sharing", 10th IEEE International Symposium on High Performance Distributed Computing, pp.181~184 (2001)
- C. C. Aggarwal, "Neural Networks and Deep Learning", pp.38~42 (2018), Springer
- H. Wang, "Wearable sensor-based human activity recognition using hybrid deep learning techniques", Security and Communication Networks, 2020, pp.1~12 (2020)[DOI]
- D. Setiawan, "Geofencing technology implementation for pet tracker using Arduino based on Android", Journal of Physics: Conference Series, 1823(1), pp.1~10 (2020)[DOI]
- R. D. Chambers, "Deep learning classification of canine behavior using a single collar-mounted accelerometer: Real-world validation", Animals, 11(6), pp.1~19 (2021)[DOI]
- P. Paci, "Understanding the Interaction Between Animals and Wearables: The Wearer Experience of Cats", Proceedings of the 2020 ACM Designing Interactive Systems Conference, pp.1701~1712 (2020)
- W. J. M. Boteju, "Deep Learning Based Dog Behavioural Monitoring System", 20203rd International Conference on Intelligent Sustainable Systems, pp.82~87 (2020)
- J. Colpoys, "Evaluation of the FitBark Activity Monitor for Measuring Physical Activity in Dogs", Animals, 11(3), pp.1~8 (2021)[DOI]
- A. Ferrari, "Human activities recognition using accelerometer and gyroscope", European Conference on Ambient Intelligence, pp.357~362 (2019)
- H. Kim, "1D-CNN-LSTM based Pet Behavior Recognition using Wearable device", The 13th International Conference on Computer Science and its Applications, pp.1~6 (2021)
- D. Van Der Linden, "Buddy's wearable is not your buddy: Privacy implications of pet wearables", IEEE Security & Privacy, 17(3), pp.28~39 (2019)[DOI]
- "SparkFun LSM6DS3 Arduino Library"[URL]