# CEPP: Perceiving the Emotional State of the User Based on Body Posture
## 4 Performance Evaluation
### Evaluation with Respect to Accuracy  
As the first step to evaluate _CEPP_, we conducted an experiment where we evaluated the accuracy of recognizing the emotional state of the user using multiple scenes from Table 2. Figure 4a illustrates the results, where the \(x\)-axis represents the index of the video frame and the \(y\)-axis depicts the similarity distance. Figure 4a indicates the result whereby the user demonstrated all of the emotions. For scenes that describe the positive and negative emotions, results are shown in Figure 4b,c. Lastly, Figure 4d shows the result of the scene, which depicts two specific emotional conditions. The emotion with the least similarity distance is indicated as the current emotional label of the user. Based on Figure 4a, we were able to verify that all of the emotional labels were recognized correctly in terms of the sequence from Table 2 by our proposed algorithm. We could clearly see that all of the emotional states were recognized properly based on the sequence of characterizing the emotional conditions. Moreover, similar results for the positive and the negative emotions could be observed. For the two specific emotional states, even though the two emotional states were categorized into either positive or negative emotions, _CEPP_ was able to recognize the emotional labels correctly. In addition, we evaluated the results from Figure 4 in terms of the frequency of appearance of each emotional state that was being recognized and its accuracy within the experimental videos. Based on the results from the figure, we could identify that the number of recognized emotional  
Figure 3: Visual results of all of the emotions in terms of binary image. (**a**) Disgust; (**b**) Fearfulness; (**c**) Happiness; (**d**) Hot anger; (**e**) Sadness. states was evenly distributed within the video as we designed in Table 2. For example, emotions recognized within the positive emotion video scene were evenly recognized, with values of 34.83%, 33.48%, and 31.69%. Similar results were observed with negative emotions, where the values were 29.34%, 37.15%, and 33.51%. As the next step for evaluating _CEPP_, we analyzed the results from Figure 4 in terms of percentages of accurate recognition of emotional state. The summary of the results is listed in Table 3. Moreover, it is depicted in Figures 5 and 6. For all of the video scenes, the accuracies in terms of correct recognition of emotional states for: (1) all of the emotions; (2) positive emotions; (3) negative emotions; and (4) specific emotions were 87.3%, 96.9%, 95.8%, and 97.0%, respectively. For specific one emotional state such as happiness, elated joy, hot anger, or boredom, in the video scenes (all of the emotions, positive emotions, negative emotions, and specific emotions), we were able to see that the percentage of accuracy was 100%. Overall, we were able to verify that the accuracy of _CEPP_ maintained above 85% for one entire video sequence. For one specific emotional state such as boredom in the video sequence of all of the emotions, the accuracy of recognizing that particular emotional state was below 80%. However, for most of the emotional states, the percentage of accuracy was above 80% and for some emotional conditions, it reached to 100%. Conclusively, with the _CEPP_ algorithm, we were able to identify the emotional state of the user based on body posture. However, it is necessary that the body area be classified and the similarity distance be computed separately to increase the overall accuracy of the algorithm. For example, the hand area needs to be identified and then compared, and the head area needs to be classified to see if the head was leaning backwards or not. Based on the similarity distance from each body region, we can analyze the similarity distance for each region separately and add these similarity distances into one whole result for recognizing the emotional state of the user. With this approach, we believe that the accuracy of _CEPP_ can be enhanced.