## 4 Experiments
### Experiments and Results
In Table 5, the experimental results prove that the proposed method achieves the best _accuracy_, _precision_, _recall_, and _F1 score_, as well as the minimum loss. TRT-Net learns the relationships between the skeletons and the relationships between frames together by connoting them as RGB colors. Therefore, it can compensate for the decrease in pose classification accuracy and shows a better classification performance than the conventional pose classification methods using multi-modal data. In other words, the conventional multi-modal methods have a problem in that the relationships between the features in the modality cannot be efficiently learned because the models simply learn only the numerical values of the data. **Experiments 2. Performance comparison between models according to fps. **  
We compared the performance of the models at various frame rates per second. Through the experiments, we compared the performance between LSTM, GRU, RNN, and the proposed TRT-Net model based on the assessment metrics of Equations (7)-(11) described earlier. A video refers to a continuous set of countless images, and sequential images are gathered to compose one video. When 30 or more consecutive frames are processed per second, humans perceive them as a natural video that is not discontinuous. Because the frame rate felt by humans as being in real time is 30 fps, we conducted the experiments by increasing the rate from 15 to 30 fps in steps of 5 fps. Figure 8 shows the comparison of the _accuracy_, _F1 score_, and error rate between the models for each setting. In the experimental results, our proposed method, TRT-Net, showed a higher accuracy and F1 score, as well as a lower error rate, than the other classification models at various frame rates per second, ranging from a low fps of 15 to a fps of 30, which by humans appears to be in real time. This proves that the algorithm can be used without a large change in accuracy in both low- and high-performance devices. Unlike other classification models, it consistently performs well without much performance change at various frame rates per second because it maintains the original data on the pre-fusion, even after fusion, while connotatively representing the relationships between the skeleton sequence data. Figure 8: Graphs showing (**a**) F1 score, (**b**) accuracy, and (**c**) error rate of four models (LSTM, GRU, RNN, TRT-Net (our)). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Color Space** & _Accuracy_ & _Precision_ & _Recall_ & _F1-Score_ &
\begin{tabular}{c} **Error Rate** \\ _(MSE)_ \\ \end{tabular} \\ \hline HSV & 84.23 & 91.54 & 85.87 & 85.33 & 0.045 \\ \hline HLS & 81.84 & 90.48 & 83.69 & 83.01 & 0.034 \\ \hline RGB & 96.95 & 97.52 & 96.74 & 96.97 & 0.008 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of behavior classification performance according to color space. **Experiments 3. Performance comparison between models according to the number of skeleton joints of a dog. **  
We know that dogs often express emotions or behavior by primarily using their body or tail. However, it can be seen that many emotions and behaviors are expressed in the face, including the dog's eyes, nose, and ears, as well as in the tail and body. In particular, the movement of the dog's ears shows their mood, condition, positive signal, or negative signal. In this study, therefore, we extracted the skeletons from the face and body of the dog through DeepLabCut [27] and conducted experiments to investigate how much impact the face and body of the dog have on the classification accuracy of its pose. As shown in Figure 9, the experiments were conducted by dividing the following cases: (a) when the ears and stifles of the dog, as proposed by [PERSON] et al. [7], were not included (14 skeleton joints); (b) when the ears of the dog, as proposed by [PERSON] et al. [5], were not included (18 skeleton joints); and (c) when the ears and stifles of the dog, as proposed in this paper, were all included (20 skeleton joints).