# Assessing the Applicability of Machine Learning Models for Robotic Emotion Monitoring: A Survey
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Authors** & **Participant No.** & **Source** & **Datasets** & **Methods** & **Accuracy Valence** & **Accuracy Arousal** \\ \hline [PERSON] et al. [125] & 32 & Tactile & Tactile & DT & 56 & 48 \\ \hline [PERSON] et al. [126] & 32 & Brain & EEG & KNN & 86.75 & 84.05 \\ \hline [PERSON] et al. [127] & 24 & Fusion & ECG, RV & SVM & 69.47 & 69.47 \\ \hline [PERSON] et al. [128] & 25 & Fusion & ECG, RV & SVM & 68.75 & 68.50 \\ \hline [PERSON] et al. [129] & 24 & Fusion & ECG, RV & SVM & 56.83 & 54.73 \\ \hline [PERSON] et al. [130] & 18 & Tactile & Tactile & Fuzzy & 69.1 & 63.1 \\ \hline [PERSON] et al. [131] & 32 & Brain & EEG & SVM & 88.9 & 89.8 \\ \hline [PERSON] et al. [132] & 32 & Brain & EEG & SVM & 91.1 & 91.3 \\ \hline [PERSON] et al. [133] & 27 & Fusion & ECG, GSR, ST, RV & SVM & 57.44 & 59.57 \\ \hline [PERSON] et al. [134] & 7 & Imaging & RGB, 3D & NN & 36.2 & 37.8 \\ \hline [PERSON] et al. [135] & 24 & Fusion & ECG, EDA, ST, Resp. & SVM & 59.57 & 60.41 \\ \hline [PERSON] et al. [136] & 32 & Imaging & RGB & NN & 87.44 & 88.49 \\ \hline [PERSON] et al. [137] & 32 & Imaging & RGB & NN & 63.5 & 61.25 \\ \hline [PERSON] et al. [138] & 12 & Fusion & EEG, RGB & Fusion & 72.8 & 77.2 \\ \hline [PERSON] et al. [139] & 32 & Brain & EEG & DT & 77.4 & 70.1 \\ \hline [PERSON] et al. [140] & 457 & Skin & EDA & NN & 73.43 & 73.65 \\ \hline [PERSON] et al. [141] & 94 & Imaging & RGB & NN & 99.4 & 96.88 \\ \hline [PERSON] et al. [142] & 58 & Heart & ECG & SVM & 78.32 & 76.83 \\ \hline [PERSON] et al. [106] & 94 & Imaging & RGB & NN & 83.79 & 83.79 \\ \hline [PERSON] et al. [116] & 40 & Brain & EEG & SVM & 59 & 68 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the included papers in the valence-arousal emotional category. difference between genuine or acted smiles, it remains to be seen how useful standalone imaging (RGB cameras) can be for mental health monitoring. However, even though emotion recognition using imaging sources scored well, there is much debate on the link between facial recognition and true emotion [145], and for our purpose of mental health monitoring, it is vital that we determine the patients' mental health accurately. Moreover, high computational power is required for deep NN methods to analyse imaging data. Thus it is unclear whether imaging is the most suited sensor for mental health monitoring. In addition, brain signal sources (EEG and ECG), are too invasive and non-consumer friendly to be used in this space [146; 147]. However, EDA-based skin signals also performed well. Out of the 80 papers, 17 used skin signals in emotion recognition, and only two used standalone EDA [81; 140], while the rest of the studies used a fusion of sensors. Notably, the three EDA emotion recognition experiments that used CNN achieved accuracies ranging from 68.5% to 95%, averaging 79%. It is likely that CNN is a good strategy for the classification of skin data, but incorporating skin-signals in robots remain challenging. The future direction of the study would investigate the feasibility and possibility of skin-based sensors to incorporate physiological signals in robots. Another signal source of interest, at 7.1% of the total, is speech audio. Audio recordings of people talking are used to classify their emotions, which can be easily applied in robots, but its classification accuracy varies a lot through speech audio. With an accuracy of over 90% in some cases [103; 105], speech audio is definitely a signal worth considering for mental health robots. We conducted an extensive survey and found some promising results. However, there are still a few limitations on which we can work in the future. The main limitation of the current study is the lack of common ground for comparison. Each experiment or study is different from another in terms of sources, ML models, and sometimes even in their evaluation metrics.