## 3 Vision-Based Approach Using a Fisheye Lens Monocular Camera
### Experiment Results and Discussion  
Using our original datasets combined with five behavior patterns, we conducted three experiments to evaluate the recognition accuracy and loss. Herein, \(A\) and \(L\), respectively, denote accuracy and loss. Moreover, we set \(A_{tra}\), \(A_{val}\), \(L_{tra}\), and \(L_{val}\) as, respectively, representing accuracy for training datasets, accuracy for validation datasets, loss for training dataset, and loss for validation datasets. Table 8 presents the combinations of behavior patterns for experiments among the designated Experiments A, B, and C. As the minimum combination, Experiment A assesses two behavior patterns: ET and RD. Experiment B represents a combination of Experiment A and SP. Experiment C targets all five behavior patterns. The first-round subsets, which comprise ET1, RD1, SP1, LC1, and ST1, are used for training. The second-round subsets, which comprise ET2, RD2, SP2, LC2, and ST2, are used for training. The number of training iterations was set as 10 epochs for all experiments. The evaluation dataset used for Experiment A comprises ET and RD of two behavior patterns. Figure 3 presents transitions of \(A_{tra}\), \(A_{val}\), \(L_{tra}\), and \(L_{val}\) in 10 epochs. Overall, the data show that \(A_{tra}\) increased steadily. The value of \(A_{val}\) soared during the fourth epoch. Although a momentary drop occurred in the fifth epoch, \(A_{val}\) increased steadily. Eventually, both \(A_{tra}\) and \(A_{val}\) reached 1.000 at the seventh epoch. By contrast, \(L_{tra}\) and \(L_{val}\) decreased steadily. Evaluation datasets on Experiment B comprise ET, RD, and SP. This combination is equivalent to the addition of SP to Experiment A. Although the subject grasped different objects in RD and SP, the feature vectors obtained using OpenPose [190] were very similar. For that reason, we specifically examined the recognition and verification of RD and SP. Figure 4 presents transitions of \(A_{tra}\), \(A_{val}\), \(L_{tra}\), and \(L_{val}\) in 10 epochs. Both \(A_{tra}\) and \(A_{val}\) increased steadily. The values of \(L_{tra}\) and \(L_{val}\) decreased, respectively, from the sixth epoch and the fourth epoch. After increasing \(L_{val}\) in the fifth epoch, it decreased from the sixth epoch. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Experiment** & **ET** & **RD** & **SP** & **LC** & **ST** \\ \hline A & ✓ & ✓ & & & \\ B & ✓ & ✓ & ✓ & & \\ C & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Combinations of behavior patterns for the respective experiments. Figure 3: Transitions of \(A_{tra}\), \(A_{val}\), \(L_{tra}\), and \(L_{val}\) from Experiment A. We conducted Experiment C using all images in the five categories. Figure 5 presents transitions of \(A_{tra}\), \(A_{val}\), \(L_{tra}\), and \(L_{val}\) in 10 epochs. The value of \(A_{tra}\) increased steadily. The value of \(A_{val}\) increased steadily, except for a slight drop in the fourth epoch. Both \(L_{tra}\) and \(L_{val}\) decreased steadily from the seventh epoch. Table 9 presents the confusion matrix obtained from Experiment C. One image of RD was falsely recognized as ET. One image of LC was falsely recognized as SP. All of the other 881 images were recognized correctly. Table 10 presents a summary of all results obtained for \(A_{tra}\), \(A_{val}\), \(L_{tra}\), and \(L_{val}\) from the respective experiments. Regarding \(A\) for all experiments, both \(A_{tra}\) and \(A_{val}\) were saturated around the seventh epoch. The \(L\) curve shows that this parameter tends to decrease if the number of iterations is increased by more than 10 epochs. Nevertheless, because of the decreased number of \(L\), overfitting might occur if the number of iterations increases for training with sufficient accuracy of \(A\). For this study, we obtained subsets of the dataset twice in a short time for one subject. Obtaining a new dataset with different dates and times is important to evaluate the overlearning and generalization that occur with our method.