## 5 Conclusions and Future Work  
In this study, we proposed TRT-Net, a visual tensor fusion network of a skeleton, in which the features of the skeleton extracted based on the body of a dog are represented in color images, where a CNN learns and classifies the behavior of the dog through color images. Furthermore, after generating a histogram according to the joint frequency based on the prediction generated by CNN and the filter visualization, the pattern learned by CNN was analyzed based on this. The proposed method converts behavior data into color images and uses them to improve the accuracy in classifying the behavioral poses of the dog. The proposed method was compared to the LSTM, GRU, and RNN, which are often \begin{table}
\begin{tabular}{c c c} \hline \hline
**Behaviors** & **Body Joints** & **Face Joints** \\ \hline \multirow{3}{*}{Walking} & front\_right\_wrist(9), & \multirow{3}{*}{right\_eye(2), left\_eye(3)} \\  & front\_left\_wrist(12), & \\  & back\_left\_paw(19) & \\ \hline \multirow{3}{*}{Standing Two legs} & front\_right\_wrist(9), & \multirow{3}{*}{right\_ear(0), left\_ear(1), left\_eye(3)} \\  & front\_left\_elbow(12) & \\ \hline \multirow{3}{*}{Standing Four legs} & front\_right\_elbow(8), & \multirow{3}{*}{right\_eye(2), left\_eye(3)} \\  & front\_right\_wrist(9), & \\  & front\_left\_elbow(11) & \\ \hline \multirow{3}{*}{Lying down} & \multirow{3}{*}{withers(6)} & right\_ear(0), left\_ear(1), right\_eye(2), \\  & & left\_eye(3) \\ \hline \multirow{3}{*}{Eating off ground} & throat(5), withers(6), & \multirow{3}{*}{left\_ear(1)} \\  & front\_right\_elbow(8), & \\  & front\_right\_wrist(9) & \\ \hline \multirow{3}{*}{Smelling} & front\_right\_wrist(9) & right\_ear(0), left\_ear(1), right\_eye(2), \\  & & left\_eye(3), \\ \hline \multirow{3}{*}{Convulsing} & \multirow{3}{*}{None} & right\_ear(0), left\_ear(1), right\_eye(2), \\  & & left\_eye(3), nose(4) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Body and face joints detected for each dog behavior. Figure 10: (**a**) \(F1\)_score_, (**b**) _accuracy_, and (**c**) error rate (_MSE_) according to the number of skeleton joints: [PERSON] et al. [7], 14 skeleton joints; [PERSON] et al. [5], 18 skeleton joints; and our method, 20 skeleton joints. conventionally used as behavior classification networks. According to the experimental results, TRT-Net demonstrated that the accuracy improves by an average of 7.9% and that the F1-score improves by an average of 7.3% compared to the conventional behavior classification networks, showing that the proposed method, TRT-Net, is more effective for dog behavior classification. Furthermore, through a visualization for each layer of the CNN model, we found that the face of a dog has the greatest impact on the recognition of its dog behavior. Although the conventional classification models achieve a good performance in various behavior classification tasks, they do not explain which patterns were learned to derive the results. However, to better understand the model and gain reliability on the results derived by the model, it is important to provide a clear and analyzable basis for the decision of the model. Finally, in this study, we conducted behavior classification experiments limited to dogs. Nevertheless, this study can be a foundation for a behavioral analysis of quadruped walking animals and can be used in various fields, including abnormal behavior recognition, rehabilitation, training, and evaluation systems. Future research plans include behavior pose classification of dogs based on automatic extraction and the collection of dog skeleton joints from videos. In addition, we plan to add more types and behaviors of dogs and improve the method in order to enable behavior recognition from new dog behavior video data, excluding the training and experimental data. [PERSON] designed the algorithm and developed the proposed algorithm and writing of the paper. [PERSON] shared her expertise concerning the overall paper. [PERSON] and [PERSON] reviewed the paper and supervised the entire process.