# Enhanced Pet Behavior Prediction via S2 GAN-Based Heterogeneous Data Synthesis
###### Abstract  
Heterogeneous data have been used to enhance behavior prediction performance; however, it involves issues such as missing data, which need to be addressed. This paper proposes enhanced pet behavior prediction via Sensor to Skeleton Generative Adversarial Networks (S2 GAN)-based heterogeneous data synthesis. The S2 GAN model synthesizes the key features of video skeletons based on collected nine-axis sensor data and replaces missing data, thereby enhancing the accuracy of behavior prediction. In this study, data collected from 10 pets in a real-life-like environment were used to conduct recognition experiments on 9 commonly occurring types of indoor behavior. Experimental results confirmed that the proposed S2 GAN-based synthesis method effectively resolves possible missing data issues in real environments and significantly improves the performance of the pet behavior prediction model. Additionally, by utilizing data collected under conditions similar to the real environment, the method enables more accurate and reliable behavior prediction. This research demonstrates the importance and utility of synthesizing heterogeneous data in behavior prediction, laying the groundwork for applications in various fields such as abnormal behavior detection and monitoring. behavior prediction; behavior monitoring; heterogeneous data; data synthesis; generative adversarial network +
Footnote †: journal: Medical  
due to obstacles or blind spots, whereas sensor data can suffer from problems such as noise [8]. Using heterogeneous data, it is possible to mitigate these issues and enhance prediction performance [9]. In research targeting humans, using various heterogeneous data is common. However, research on behavior prediction that targets pets has gradually progressed because of difficulties in data collection and the diversity of pet behaviors. Herein, we propose a new method for predicting pet behavior by synthesizing heterogeneous data using the Sensor to Skeleton Generative Adversarial Networks (S2 GAN) model. The heterogeneous data used in this approach were collected using commonly used cameras and wearable devices for monitoring pets. When dealing with heterogeneous data, it is crucial to note that even partially missing data can cause significant problems for the model. Therefore, our method based on the S2 GAN model--a generative adversarial network that enables the transformation of sensor data into the feature of skeleton data--aims to overcome the limitations of video data by supplementing missing data with a small data collection range. Even if the feature of skeleton generation is not accurately replicated, there is still value in reflecting these features. This approach aims to expand the recognized behavioral range of pets, allowing for accurate modeling and prediction of pet behavior in various environments. The main contributions of this research are as follows. * Improving the accuracy and reliability of behavior prediction through efficient preprocessing and integration for predicting complex behaviors of pets. * Exploring the possibility of enhancing behavior prediction by addressing existing missing data issues through the S2 GAN-based synthesis of heterogeneous data. * Demonstrating the practicality of the proposed method by applying it to data collected in real-world environments. This study will prompt a re-evaluation of existing approaches in the field of pet behavior prediction and contribute to the development of more precise and effective prediction models. In addition, it will lay an important foundation for advancing related research and improving pet welfare. The remainder of this article is structured as follows: Section 2 reviews the related research in the field of pet behavior prediction and explains the background and necessity of this study. Section 3 details the principles of pet behavior prediction via the proposed S2 GAN-based heterogeneous data synthesis method and discusses the design and implementation process of the developed behavior prediction method.