## 4 Experiments
### Experimental Results
#### 4.2.2 Behavior Recognition Using Sensor Data  
Experiments on sensor data were conducted by combining the five statistical features specified in Section 3.2, and it was confirmed which combination of features had the  
Figure 8: Confusion matrix of behavior recognition by dog detection methods using video data: (a) FasterRCNN; (b) YOLOv3; (c) YOLOv4. The bluer the cell, the higher the accuracy of dog recognition.  
Figure 7: Distribution of detection by dog behavior per method: (a) FasterRCNN; (b) YOLOv3; (c) YOLOv4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dog Detection Method** & **Accuracy** & **Precision** & **Recall** & **F-Score** \\ \hline FasterRCNN & 86.3\% & 0.91 & 0.86 & 0.88 \\ YOLOv3 & 84.7\% & 0.9 & 0.85 & 0.86 \\ YOLOv4 & **89.2\%** & **0.92** & **0.89** & **0.9** \\ \hline Average & **86.7\%** & **0.91** & **0.87** & **0.88** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of behavior recognition performance evaluation by dog detection methods using video data.  
greatest effect on performance. Table 6 shows the experimental results for the case where no features were selected (None), those for the single-feature case, and the ten feature combinations that showed the best performance among multiple features from a total of 32 feature combinations.  
As a single feature, \(f_{std}\) performed well, but when combined with other features, \(f_{amp}\) was the best complementary feature. In contrast, \(f_{var}\) degraded performance or did not affect performance. In the case of multiple features, since the degree of complementation between features was different, there was a difference in performance even when features were similarly combined; however, the performance was generally improved when \(f_{amp}+f_{skew}\) was included. As a result, the combination of multiple features elicited better performance than the result using only raw data. Among them, the combination that showed the best performance was \(f_{mean}+f_{var}+f_{std}+f_{amp}+f_{skew}\), which showed an accuracy of 56.3%.  
Figure 9 shows the confusion matrix for None with no features selected and for the best feature combination in Table 6. The sensor data showed that behavior recognition for B4 and B6 was well achieved, and it was confirmed that the recognition rate for B2 was improved when statistical features were included. Since the device worn around the neck was sensitive to the movement of the head, it is judged that the recognition rate was high because these behaviors feature fewer head movements than other behaviors.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Selected Features**} & \multirow{2}{*}{
\begin{tabular}{c} **Feature** \\ **Length** \\ \end{tabular} } & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F-Score**} \\ \hline None & 6 & 0.455 & 0.56 & 0.46 & 0.49 \\ \hline \(f_{mean}\) & 12 & 0.383 & 0.6 & 0.38 & 0.46 \\ \(f_{var}\) & 12 & 0.443 & 0.76 & 0.44 & 0.48 \\ \(f_{std}\) & 12 & 0.509 & 0.62 & 0.51 & 0.55 \\ \(f_{amp}\) & 12 & 0.479 & 0.63 & 0.48 & 0.54 \\ \(f_{skew}\) & 12 & 0.395 & 0.66 & 0.4 & 0.47 \\ \hline \(f_{mean}+f_{amp}\) & 18 & 0.521 & 0.63 & 0.52 & 0.56 \\ \(f_{var}+f_{amp}\) & 18 & 0.515 & 0.62 & 0.51 & 0.54 \\ \(f_{amp}+f_{skew}\) & 18 & 0.515 & 0.63 & 0.51 & 0.55 \\ \hline \(f_{mean}+f_{amp}+f_{skew}\) & 24 & 0.515 & 0.61 & 0.51 & 0.55 \\ \(f_{std}+f_{amp}+f_{skew}\) & 24 & 0.551 & 0.63 & 0.55 & 0.57 \\ \hline \(f_{mean}+f_{var}+f_{std}+f_{amp}\) & 30 & 0.503 & 0.68 & 0.5 & 0.56 \\ \(f_{mean}+f_{var}+f_{std}+f_{skew}\) & 30 & 0.551 & 0.7 & 0.55 & 0.61 \\ \(f_{mean}+f_{std}+f_{amp}+f_{skew}\) & 30 & 0.539 & 0.61 & 0.54 & 0.56 \\ \(f_{var}+f_{std}+f_{amp}+f_{skew}\) & 30 & 0.539 & 0.67 & 0.54 & 0.58 \\ \hline \(f_{mean}+f_{var}+f_{std}+f_{amp}+f_{skew}\) & 36 & **0.563** & **0.76** & **0.56** & **0.62** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of behavior recognition performance evaluation by selected features using sensor data.