# Correlation between Human Emotion and Temporal-Spatial Contexts by Analyzing Environmental Factors
## 4 Experiments and Results  
The experiments were performed twice for each subject group. In the first group, 30 subjects participated. In the second group, 66 subjects participated in the experiments. Thirty subjects participated voluntarily in the first experiment; these subjects consisted of postgraduate students in the Department of Computer Science, Emotion Engineering, and Mobile Software from Sangmyung University (male = 16, female = 14, mean age = \(\pm\)27.1). The procedure of the experiment consisted of three parts as described in the experimental scenarios: taking videos and estimating the subjective evaluations, as shown in Figure 9.  
Firstly, the experimental scenarios were described for 3-5 min to 30 subjects before the start of the experiment. In addition, all 30 subjects were individually provided with smartphones. We also explained how to use the development application installed on the smartphone, as described in Figure 9. Secondly, we instructed the subjects to take videos of the surrounding environment while walking around the university by clicking the camera images in the middle of the screen, as shown in Figure 7a. In this case, the actual surrounding environment to capture via videos was recorded for 5 s in one shot. Lastly, subjective evaluation was performed according to emotional states on the axes of \"Pleasant-Unpleasant\" and \"Arousal-Relaxation\" while re-watching the scenes for 30 s using the bar graph on the bottom of the screen, as shown in Figure 7a. In the subjective emotional  
Figure 8: Fully connected dual SVR networks with nine inputs and two outputs. Figure 9: Data acquisition process for experiment. evaluation, the values were estimated in a range from \(-1\) to \(1\) on both the \"Pleasant-Unpleasant\" X-axis and the \"Arousal-Relaxation\" Y-axis. This process was performed 50 times by each subject. For data collection, a total of 50 videos were acquired per subject. Consequently, a total of 1500 pairs of videos and subjective questionnaire evaluations were collected by 30 subjects (30 subjects \(\times\) taking videos and subjective evaluations 50 times = collecting 1500 pairs). According to the previous section regarding the sequence of SVR-estimator execution, the 1500 collected datasets were separated into the training and test sets. In the experiment, 1000 of the 1500 data points were allocated to the training set, and the remaining 500 were allocated to the test set. In addition, fully connected dual SVR inference networks were used. The data type consisted of nine input data points extracted from videos and two output data points derived from SVR #1 and SVR #2. Specifically, the value of the x-axis as \"Unpleasant-Pleasant\" was determined using SVM #1. Similarly, the value of the y-axis as \"Arousal-Relaxation\" was determined using SVM #2. In this report, the value of root mean square (RMS) error was calculated between the position of value used by the SVR estimator and the results for the subjective evaluation for measuring the accuracy of the proposed fully connected SVR network-based emotional evaluation owing to the surrounding environmental factors. The RMS error was calculated using the absolute value between x and y. For more details, X and Y were calculated by subtracting the absolute value of the square values of the validation and prediction according to the SVR estimator in \"Pleasant-Unpleasant\" and \"Arousal-Relaxation\". Moreover, this software was implemented using C++ with OpenCV in real-time on a PC running Windows 10 OS, an Intel i5-6200U CPU at 2.4 GHz, 8 GB RAM, and a 23-inch display. In addition, we constructed various cases according to the number of input four feature categories and analyzed the emotion estimation accuracy for each case. In this analysis, the nine features are categorized into four groups such as (1) Temporal complexity, (2) Spatial complexities (Horizontal and Vertical Edge), (3) Colors (Hue, Saturation, Intensity, Contrast), and (4) Sounds (Amplitude, Frequency).