# Assessing the Applicability of Machine Learning Models for Robotic Emotion Monitoring: A Survey
## 4 Results and Discussion
However, with an accuracy of up to 90% in some cases, speech is definitely a signal worth considering for mental health robots. On the other hand, the tactile signal did not perform well at all. With an accuracy of 22.30%, tactile signals were the worst performer among the discrete signals. A similar thing can be noticed in Figure 6 as well--tactile signals had very low accuracies. Accuracies procured from the eye signals were unsatisfactory as well (52.70% and 59.60%). However, the lung signals and the muscle signals are particularly worth mentioning in this regard as they had a few data points, and it would not be constructive to reach any conclusion based on the average performances. It is worthwhile to mention that most of the fused source signals had accuracies over 80% and more than 90% in some cases. Therefore, another interesting approach to emotion recognition can be fusing signals from different sources  
In Figure 6, the accuracies of valence are plotted against the accuracies of arousal. Different colors, shapes, and sizes represent different methodologies, sources, and the number of participants, respectively. For accuracies provided in a range, the maximum value was used in both figures. The only well-performing signal is the brain signals. None of the other signals provided a good accuracy value. If we consider the 10 best accuracies, 8 of them were from brain signals. The brain signals, including other associated signals, might be useful for diagnosing and maintaining other brain disorders, for example multiple sclerosis [122; 123] and autism spectrum disorder [124]. Figure 6: Scatter plot for the Valence-Arousal emotional category. For Figures 5 and 6, neural network-based methods outperformed the other methods. K-Nearest Neighbours and Support Vector Machine also performed well in both emotional categories. However, Decision Tree-based methods slightly outperformed KNN and SVM in the discrete emotional category. The highest accuracy was scored by the neural network-based methods, but the most common method found in our studies, with 30% of the total, is SVM. There could be two reasons for the comparatively lower number of papers using NN. First, training NNs or any deep networks require a large number of data. This is hard to come by with physiological signals. Even the largest sample size in all of the papers was 457 for an EDA-based experiment. However, image sets can have potentially thousands of faces, and this does not count video datasets. Another reason for fewer experiments with neural network-based methods could be computational effort, as SVM is a faster process than neural network methods [143]. It is also noticeable that the accuracy of imaging (RGB sensors) appears much higher than other sensors, representing 40% of the papers with the highest accuracies. However, while facial expression accuracy is very high compared to other physiological categories, there is a key difference in emotional validation. At face value, the facial expression is a sort of derived signal where people can counterfeit their smile. Unless we can differentiate between fake and genuine facial expressions, the emotional expressions we get from patients might not always represent their true mental state. However, one could argue that if robots were used in a person's home environment, where they were more likely to be relaxed, they would most likely capture the genuine emotions of the person. Furthermore, there is evidence that facial muscles activate differently depending on whether the person is genuine or acting a smile [144]. However, since none of the papers investigated the