# Human-Touch-Inspired Material Recognition for Robotic Tactile Sensing
## 4 Results
### Material Recognition  
Consistent with property recognition, Linear SVM was used to determine the principal components. As shown in Figure 4, the accuracy increased, with the number increasing as a result, and 11 principal components were selected to form the feature vector. The accuracies and time overheads of the classifiers are provided in Table 4.  
From Table 4, it can be seen that the accuracies of the six classifiers are not below 80%, and MLP provides the best accuracy (90.28%). The performances of these classifiers proved the effectiveness and robustness of the tactile data and feature extraction. The confusion matrix in Figure 6 was plotted based on the MLP classifier, and the matrix shows that most mis-classified errors occur in materials with the same or similar properties. For example, PVC was confused with rubber, as they are soft and smooth. Additionally, acrylic was mistakenly classified as wood, since they are both hard. The time overheads of material and property recognitions in each process are mainly the same, except for classification. Material recognition took much more time in classification, although the overhead was still the small in relation to the data-processing.  
Figure 6: Confusion matrix of material recognition based on the MLP classifier. The columns indicate the predictions and rows indicate the real classification.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Recognition** & **Processes** & **Linear SVM** & **RBF SVM** & **kNN** & **LDA** & **NB** & **RF** & **MLP** \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Property \\ Recognition \\ \end{tabular} } & Data Reading (ms) & 19.60 & 19.86 & 19.78 & 19.75 & 20.58 & 19.53 & 20.13 \\  & Dimensionality Reduction (ms) & 11.49 & 10.80 & 11.58 & 11.51 & 11.03 & 12.63 & 10.75 \\  & Feature Extraction (ms) & 4.56 & 4.33 & 4.64 & 4.66 & 4.35 & 4.83 & 4.53 \\  & Classification (\(\mathrm{\SIUnitSymbolMicro s}\)) & 0.69 & 22.92 & 17.36 & 0.69 & 1.39 & 2.78 & 3.47 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Material \\ Recognition \\ \end{tabular} } & Data Reading (ms) & 19.85 & 19.94 & 19.69 & 19.84 & 19.98 & 21.04 & 20.58 \\  & Dimensionality Reduction (ms) & 12.01 & 11.45 & 11.76 & 11.83 & 10.74 & 11.09 & 11.34 \\  & Feature Extraction (ms) & 4.88 & 4.56 & 4.85 & 5.04 & 4.23 & 4.36 & 4.83 \\  & Classification (\(\mathrm{\SIUnitSymbolMicro s}\)) & 1.39 & 48.62 & 211.81 & 1.39 & 2.08 & 118.06 & 3.47 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The run time of the processes of material recognition.  
This paper used slide action to explore the material. However, it is difficult to keep the interactive force constant during the slide. Although the sequences extracted from the raw tactile data were standardized to make themselves independent of the interactive force, the robustness of our method needs to be validated. Here, 100 g, 150 g, 250 g, and 300 g weights were used as the loads, then 30 sets of sample data were recollected for each material to test the robustness. The new data were allocated into 4 databases: \(\mathbf{D}_{100}\), \(\mathbf{D}_{150}\), \(\mathbf{D}_{250}\), and \(\mathbf{D}_{300}\), and the trained kNN classifier, and MLP classifier were used for the property classification and material classification. The results in Table 6 show that our method is robust enough to recognize materials under different interactive forces.  
In order to test the real-world applicability of the method, the trained MLP classifier was used to identify other materials that are the same or similar to the materials used in this paper but from different objects. The materials are wood, sponge, cotton, iron, sandpaper (150 Grit), and ceramics. For each material, 30 data samples were collected. The recognition results are shown in Table 7. Although the recognition of other materials is not as accurate as before, it is also acceptable.