## 3 Vision-Based Approach Using a Fisheye Lens Monocular Camera
### Setup and the Obtained First Dataset  
Table 4 presents some major specifications of the fisheye lens camera (RealSense T265; Intel Corp.; Santa Clara, CA, USA) used for this study. The FoV of this camera is \(63\pm 5\) deg, which is approximately twice that of an ordinary monocular camera. The camera body is 12.5 mm long \(\times\) 108 mm wide \(\times\) 24.5 mm high. This compact camera can reduce the psychological burdens of a subject that might result from awareness of a camera during an experiment. Nevertheless, because of this wide FoV, the subject becomes extremely small if the camera is in a distant position. In addition, image distortion is extensive at the image edges. For this property, we set \(x=2.0\) in specific consideration of this camera. We obtained images for the dataset in our university laboratory. The 100 m\({}^{2}\) room, which has 17 students and 2 researchers, is an open space with no blocking structure except for 1.2 m high partitions. In a meeting corner of this room, we set up an environment for obtaining images. The ultimate goal of this line of research is to infer a person's emotional state based on their displayed behavior patterns. Human emotions have individual and often idiosyncratic differences that present implications about the person's affect, well-being, and social relations [216]. For this study, we aim at developing an individual estimation model that is suitable and specialized for each subject without using a common and general estimation model. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Specifications** \\ \hline Imaging sensor & OV9282 \\ Lens size & 14 inch \\ Pixel size & 3 \(\times\) 3 \(\upmu\)m\({}^{2}\) \\ FoV & 163 \(\pm\) 5 deg \\ Resolution & W1280 \(\times\) H800 pixels \\ Frame rate & 120 fps \\ IMU & BMI055 \\ Body dimensions & L12.5 \(\times\) W108.0 \(\times\) H24.5 mm \\ Weight & 55 g \\ Mean power consumption & 1.5 W \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Major specifications of the fisheye lens camera (RealSense T265; Intel Corp.). \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Setting Values** \\ \hline Learning iteration & 50 epochs \\ Batch size & 4 \\ Validation split & 0.2 \\ Num. input layer & 224 \(\times\) 224 \(\times\) 3 units \\ Num. output layer & 2, 3, or 5 units \\ Optimization algorithms & Adam [213] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: VGG-16 parameters and their setting values. The target behavior patterns represent five categories: eating (ET), reading (RD), operating a smartphone (SP), operating a laptop computer (LC), and sitting (ST). Herein, ST stands for a state of doing nothing while sitting. Moreover, ST might represent simple relaxation, taking a break, taking a nap, thinking, or dreaming. Recognition of human activities that involve great amounts of motion during standing has already been addressed in a report described by [PERSON] et al. (2000). Therefore, for this study, we specifically examined slight motions that are made during sitting. After we explained the ethics and purpose of this experiment, the subject performed these five actions in order. The interval of each behavior was set as approximately 20 s.  
Table 5 presents details of the obtained images as our original dataset, which contains 10 subsets. Each subset was obtained twice for each behavior pattern. The behavior periods varied slightly because of the lack of a timer setting. Although all images included the subject, several unconvertible images of feature vectors occurred in LC and ST. All feature vectors were extracted from three categories: ET, RD, and SP. The conversion ratio for all datasets was 0.92 for this dataset. Table 6 presents major performance specifications of our computer, which was used to execute OpenPose (190) and VGG-16 (159). For this study, we set \(n=10\) with emphasis on our computer environment. Table 7 presents details of the computational time necessary to calculate feature vectors using OpenPose.