Dog Behaviour Analysis

Abstract

Dogs show a high degree of heterogeneity in their behavior patterns because of the close association between humans and dogs in many capacities – domesticated pets, assistance animals and experimental subjects. Heterogeneity of dogs\' behavior can be attributed both to intrinsic and extrinsic reasons including, among others, breed, genetics, age, environment, human interaction, training programs. Accurate dog behavior prediction is becoming increasingly important from the point of view of animal welfare, safety, and training efficiency. In this study, we attempt to examine different machine learning models that can help predicting dog behavior patterns based on structured datasets. By analyzing certain behavioral and contextual factors, this study intends to evaluate the efficacy of models such as Artificial Neural Networks and Random Forests. The general idea is to find some patterns and predictors, which can work with any individual dogs and allow predicting their behavior. Behavioral tests and observation were popular tools for evaluating temperaments in animals, however, the results of these practices are often subjective, inconsistent and lack scalability. Machine learning offers an innovative approach to behavior pattern analysis which involves large volumes of data processing. Supervised machine learning classifiers will be trained and tested by using certain evaluation metrics including accuracy, precision, recall, F1-score. From the results, it can be noted that there is no model that is superior to all other models. Every model has its strengths based on the behavior class it will be applied to and the type of features used. This highlights the significance of choosing the best models and developing suitable features in predicting behaviors. In conclusion, this study adds value to computational ethology by proposing the use of AI in behavioral science.

Introduction

The text discusses the importance of understanding and predicting dog behavior using machine learning techniques. Dogs are highly intelligent and socially responsive animals whose behavior varies due to factors such as breed, genetics, age, training, environment, and socialization. Accurate behavioral assessment is important in animal science, veterinary medicine, rescue shelters, and working-dog organizations. Traditional behavior evaluation methods are often subjective, time-consuming, and inconsistent, creating a need for data-driven approaches.

The study uses an open-source dog behavior dataset collected through accelerometer and gyroscope sensors attached to dogs’ backs and necks. The dataset contains motion data, timestamps, dog identifiers, experimental information, and behavior labels such as walking and eating. These multimodal sensor readings provide detailed information for behavior classification.

Three machine learning models are explored:

• Random Forest (RF): An ensemble learning method that combines multiple decision trees to improve classification accuracy. It is robust, handles high-dimensional sensor data well, and provides feature importance information, but it cannot naturally capture temporal relationships in sequential data.
• Artificial Neural Network (ANN): A deep learning model capable of learning complex and nonlinear relationships in sensor data. It is flexible and effective for large datasets but requires careful tuning and is difficult to interpret.
• Long Short-Term Memory (LSTM): A specialized recurrent neural network designed for sequential and time-series data. Since dog behavior develops over time, LSTM can capture movement patterns and temporal dependencies, making it particularly suitable for recognizing activities such as walking,

Conclusion

The objective of the current research paper is to examine the efficiency of machine learning and deep learning methods used for predicting the behavior of dogs by employing information obtained from motion sensors using wearable devices. Several machine learning algorithms, such as Random Forest, ANN, and LSTM, were implemented and evaluated to measure their efficiency in terms of their ability to predict certain behavioral patterns, namely, walking, feeding, smelling, and sleeping. LSTM proved to be the most efficient model as far as its accuracy is concerned (the figure is about 85%), while the loss was minimal (the value of 0.33). Such a high level of accuracy can be explained by the model\'s ability to find connections in time between the observations. As behavior is an ongoing process, LSTM was able to recognize better the movement patterns between different activities. As for the Random Forest model, its performance was also impressive and resulted in stable and accurate predictions with high interpretability. This classifier efficiently processed structured sensor data and outperformed the ANN model in terms of accuracy. Nevertheless, the ANN model was able to find complex non-linear patterns in the dataset; however, its performance level was slightly worse than those of the Random Forest and LSTM models since it lacks temporal memory. Thus, the results show that although the application of classical machine learning techniques can result in successful classification of dog behavior, deep-learning approaches prove to be more efficient for analyzing the time-series data. These findings prove that AI models can significantly contribute to the development of systems designed for monitoring and predicting canine behavior. In conclusion, this paper demonstrates that the integration of sensor devices and AI models can facilitate behavioral analysis of dogs to a significant extent. Future research on the topic could include the use of bigger datasets, real-time analysis tools, and more efficient models like the GRU or CNN-LSTM ones.

References

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Copyright

Copyright © 2026 Ajay Kumar Meena. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.