Abstract
The safety and welfare of companion animals such as dogs has become a large challenge in the last few years. To assess the well-being of a dog, it is very important for human beings to understand the activity pattern of the dog, and its emotional behavior. A wearable, sensor-based system is suitable for such ends, as it will be able to monitor the dogs in real-time. However, the question remains unanswered as to what kind of data should be used to detect the activity patterns and emotional patterns, as does another: what should be the location of the sensors for the collection of data and how should we automate the system? Yet these questions remain unanswered, because to date, there is no such system that can address the above-mentioned concerns. The main purpose of this study was (1) to develop a system that can detect the activities and emotions based on the accelerometer and gyroscope signals and (2) to automate the system with robust machine learning techniques for implementing it for real-time situations. Therefore, we propose a system which is based on the data collected from 10 dogs, including nine breeds of various sizes and ages, and both genders. We used machine learning classification techniques for automating the detection and evaluation process. The ground truth fetched for the evaluation process was carried out by taking video recording data in frame per second and the wearable sensors data were collected in parallel with the video recordings. Evaluation of the system was performed using an ANN (artificial neural network), random forest, SVM (support vector machine), KNN (k nearest neighbors), and a naïve Bayes classifier. The robustness of our system was evaluated by taking independent training and validation sets. We achieved an accuracy of 96.58% while detecting the activity and 92.87% while detecting emotional behavior, respectively. This system will help the owners of dogs to track their behavior and emotions in real-life situations for various breeds in different scenarios.
Highlights
The rapid development of digital information processing technology provided an opportunity to explore the behavior of the animals aside from direct observation by humans [1]
The complete process was divided into four different sections; namely, the Figure shows the complete process of the development of the activity and emotion detection systems for household pets
This study presented a robust by using an incremental patterns, and is recommended for real-life situations, because the emotional pattern can complete be machine learning algorithm
Summary
The rapid development of digital information processing technology provided an opportunity to explore the behavior of the animals aside from direct observation by humans [1]. One approach for monitoring these behaviors needs a continuous collection of data by using human observers. This approach is not ideal because (1) continuous observation by. Sci. 2019, 9, 4938 a human and interfering with the normal life of pets by continuous observation can have a negative impact on a pet’s behavior and health. The other approach for the collection of data is by leveraging an automated system without interfering with pets’ normal lives. The automated system that is available uses sensors and it can be directly attached to dogs; or it creates an environment using the sensors for the collection of data [4]
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