# Assessing the Applicability of Machine Learning Models for Robotic Emotion Monitoring: A Survey
## 4 Results and Discussion
The graphs were plotted based on the data we assembled from our study across the 80 papers. Four papers ([50; 74; 75; 76]) did not provide a separate accuracy for valence \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Authors** & **Participant No.** & **Source** & **Dataset** & **Methods** & **Highest Accuracy** \\ \hline [PERSON] et al. [86] & 70 & Imaging & RGB & Fusion & 96.26 \\ \hline [PERSON] et al. [87] & 30 & Fusion & EEG, ECG, Resp., & & \\  & & & EDA & SVM & 84.60 \\ \hline [PERSON] et al. [88] & 27 & Fusion & EEG, ECG, Resp., & & \\  & & & EDA & SVM & 84.62 \\ \hline [PERSON] et al. [89] & 28 & Imaging & RGB, ST & LDA & 85.75 \\ \hline [PERSON] et al. [90] & 14 & Fusion &
\begin{tabular}{c} Motion, RGB, \\ Radio \\ \end{tabular} & KNN & 84.8 \\ \hline [PERSON] et al. [91] & 487 & Speech & SA & Fusion & 94.50 \\ \hline [PERSON] et al. [92] & 221 & Imaging & RGB & Fusion & 91 \\ \hline [PERSON] et al. [93] & 1192 & Imaging & RGB & NN & 44.9 \\ \hline [PERSON] et al. [94] & 52 & Fusion & SA, RGB & NN & 86.89 \\ \hline [PERSON] et al. [95] & 1 & Muscle & EMG & NN & 99.69 \\ \hline [PERSON] et al. [96] & 40 & Fusion & ECG, GSR & NN & 93 \\ \hline [PERSON] et al. [97] & 20 & Brain & EEG & SVM & 93.13 \\ \hline [PERSON] et al. [98] & 8 & Fusion & EEG, HR, EDA & KNN & 99.5 \\ \hline [PERSON] et al. [99] & 4 & Speech & SA & DT & 87.85 \\ \hline [PERSON] et al. [100] & 4 & Imaging & 3D & KNN & 92.8 \\ \hline [PERSON] et al. [101] & 24,336 & Imaging & RGB & NN & 90.14 \\ \hline [PERSON] et al. [102] & 25 & Heart & HR & DT & 84 \\ \hline [PERSON] et al. [103] & 339 & Speech & SA & NN & 93 \\ \hline [PERSON] et al. [104] & 3 & Imaging & RGB & NN & 99.9 \\ \hline Z\(\rm\ddot{\rm{}}\)arevashe et al. [105] & 28 & Speech & SA & DT & 99.55 \\ \hline [PERSON] et al. [43] & 30 & Imaging & RGB & LDA & 94.67 \\ \hline [PERSON] et al. [106] & 94 & Imaging & RGB & NN & 91.02 \\ \hline [PERSON] et al. [107] & 32 & Fusion & GSR, SA, ECG & KNN & 86 \\ \hline [PERSON] et al. [108] & 27 & Imaging & RGB & DT & 99 \\ \hline [PERSON] et al. [109] & Sim. & Imaging & RGB & NN & 82.5 \\ \hline [PERSON] et al. [110] & Sim. & Brain & EEG & LDA & 99.44 \\ \hline [PERSON] et al. [111] & 10 & Fusion & RGB, SA & NN & 89 \\ \hline [PERSON] et al. [112] & Sim. & Brain & EEG & SVM & 99.82 \\ \hline [PERSON] et al. [113] & Sim. & Imaging & RGB & NN & 80.6 \\ \hline [PERSON] et al. [114] & 12 & Imaging & RGB & FGSM & 93.31 \\ \hline [PERSON] et al. [115] & 10 & Speech & SA & NN & 91.32 \\ \hline [PERSON] et al. [116] & 40 & Fusion & EEG & NN & 89 \\ \hline [PERSON] et al. [117] & 24 & Imaging & RGB & NN & 91 \\ \hline [PERSON] et al. [118] & 70,000 & Imaging & RGB & NN & 93 \\ \hline [PERSON] et al. [119] & 84 & Lungs & KINECT & SVM & 99.67 \\ \hline G\(\rm\ddot{\rm{o}}\)m\(\rm\ddot{\rm{u}}\)sl\(\rm\ddot{\rm{u}}\) et al. [120] & 15 & Fusion & EEG, BVP, ST, SC & Fusion & 94.58 \\ \hline [PERSON] et al. [121] & 24 & Speech & SA & NN & 83.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._In Figure 5, the accuracies of the methods are plotted against the source of the signals. Different colors denote the methodologies used in the experiments. If any of the experiments were conducted under different experimental settings, the best results from all of those settings were considered. Further, if any of the experiments used multiple sources altogether, they were considered a fusion source. The highest level of accuracy was achieved from imaging signals. Amongst the 27 imaging signals, 25 of the signal studies resulted in above 80% accuracy. While two of the imaging signals showed poor accuracies (44.90% and 46.70%), the rest of the imaging signals showed accuracies ranging from 80.33% to 99.90%. Therefore, facial imaging can potentially be the most prevalent signal for emotion recognition. The brain, heart, and skin signal sources provided good accuracies of above 60-70%. Another signal of interest is speech audio, for which classification accuracies varied a lot, from 55% to 99.55%.