# Assessing the Applicability of Machine Learning Models for Robotic Emotion Monitoring: A Survey
## 4 Results and Discussion  
Following data extraction, we assembled the classification accuracy results from the papers and identified different signal sources. We found a total of 18 signal sources generated from different parts of the human body. Applying different machine learning methods, the studies attempted to identify or monitor human emotions. Among all papers, only one [64] used fully synthesized data, where no participants were involved. Due to many experiments involving multiple sensors, the number of signal sources and sensors is greater than the number of experiments. In total, 112 signals were studied across different physical and physiological sources, namely Brain, Lung, Skin, Heart, Muscle, Imaging, Speech, Tactile, etc. (Details will be found in Supplementary Material). The choice of the classifier plays a key role in accurately classifying emotions. Therefore, in various experiments, we have come across multiple supervised, unsupervised, and hybrid classifiers. To allow for accurate comparisons, papers are split into two main categories of emotion: discrete and valence-arousal. However, even among discrete emotions studies, there are intensity experiments, e.g., anger intensity [65] and stress level [66]. This adds another dimension to an emotion classification task. Since this is not the same kind of classification as mapping out a user state to the six basic emotions, and all results in a shared category should be comparable to each other, this kind of experiment is categorized as \"other\". Gesture recognition tasks that are not validated to emotions are also in the other category. The distribution of emotion classification type is illustrated in Figure 4.  
The focus of our study was detecting emotion correctly for better mental health monitoring. Amongst the 80 papers, 70 papers provided single or a range of accuracy percentages as an evaluation matrix. The remaining eight studies used different evaluation metrics for their experiments and, therefore, are excluded from Figures 5 and 6. [PERSON] et al. [64] calculated their results in terms of sensitivity and specificity, whereas [PERSON] et al. [65] used RMSE to evaluate the accuracy of predicted ratings. [PERSON] et al. [67] measured their performance in terms of area under curve where the result varied from 55% to 62%. The experiments performed by [PERSON] et al. [68] and [PERSON] et al. [69] were evaluated in terms of the correlation coefficient. On the other hand, [PERSON] et al. [45] evaluated their performance on the basis of mean average precision, whereas [PERSON] et al. [70] and [PERSON] et al. [71] utilized predictive and statistical data analysis rather than classification. Few studies did not include any evaluation matrix at all, as their aims were beyond classification tasks. [PERSON] et al. 2018 [72] proposed a perceptual framework for emotion recognition. [PERSON] et al. 2019 [73] showed that socio-emotional brain areas do not react to effective  
Figure 4: Used emotional model for recognition tasks. touch in infants. These provide conclusions to their research questions but do not yield a percentage accuracy figure. Although discrete emotion classification experiments take up 54% of the total, it is nearly matched by experiments mapping emotions to the valence-arousal plane (30%). The remaining 16% are emotion models that do not fit any of these emotion-label categories. Most experiments classified emotions using discrete labels, such as happy, neutral, sad, or the continuous valence-arousal plane. How studies classified emotions using the plane varied; however, some split the V-A plane into quadrants to create four emotion labels (Figure 2). Meanwhile, some others measured distance along the valence and arousal axes. To visualize the findings of our survey, we created two separate scatter plots for the discrete (Figure 5) and valence-arousal (Figure 6) categories. Highest accuracies for the discrete and the V-A categories are shown in Tables 1 and 2.