# Human-Touch-Inspired Material Recognition for Robotic Tactile Sensing
## 3 Methodology
### Data Collecting and Dimensionality Reduction  
When human skin slides along an unknown material, the four mechanoreceptors cooperate with each other to sense the stimuli dynamically and generate the corresponding tactile signals [25]. The stimuli they sensed are shown in Table 2. To collect tactile data with different frequencies, as skin does, the data were recorded at 100 Hz, which mainly covers the frequency range of the stimuli perceived by the mechanoreceptors. At this frequency, the sensor slid across the material for 1 s at a speed of 2.5 mm/s, and the sensor can record 100 tactile images after the slide action. However, the high-density sensor produces 193600-dimensional data in each exploration. To process the data efficiently, three characteristics were designed to represent the image, and their time sequences were calculated to reduce the amount of data. The first sequence \(\mathbf{S}_{\text{a}}\) consists of areas of 100 tactile images, the second sequence \(\mathbf{S}_{\text{v}}\) consists of variances of 100 tactile images, and the third sequence \(\mathbf{S}_{\text{s}}\) consists of sums of the forces in the 100 tactile images. Numerically, the tactile image area is the number of the force sensing elements that sensed the forces, the tactile image variance is the variance of the pixels, and the sum of forces is the sum of the image intensities. These three sequences were used as the sample data, since they are relevant to the compliance and texture properties of the material. According to contact mechanics [31], the approximate information of the contact area (\(\mathbf{S}_{\text{a}}\)), pressure distribution (\(\mathbf{S}_{\text{v}}\)) and force value (\(\mathbf{S}_{\text{s}}\)) are relevant to the compliance of the material, while detail information on sequence \(\mathbf{S}_{\text{s}}\) is relevant to the texture properties [32] The three sequences of a random sample were displayed in Figure 3, with the average sequences of all samples. As demonstrated in Figure 3, it is obvious that all materials behaved differently in the slide action. The sequences of the random sample presented different consistencies of materials, for example, the \(\mathbf{S}_{\text{s}}\) of PVC changed very quickly, and the magnitude of the change was large, while the \(\mathbf{S}_{\text{s}}\) of FSR was almost unchanged. By observing the average sequences, it is clear that the materials are statistically discriminable. Therefore, extracting features that characterize the change and average of sequences is an efficient approach to improving the performance of our method. In this study, the means of three sequences and the changes of \(\mathbf{S}_{\text{s}}\) were concerned with the feature extraction. Moreover, in order to make the method independent of the interactive force, the three sequences were standardized by removing the mean and scaling to unit variance. In addition, 120 sets of sample data were collected for each material, which means \(120\times 12\times 100=144,000\) tactile images were used to build the database.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Classification Basis**} & **Pacinian** & \multirow{2}{*}{**Ruffini Corpuscle**} & \multirow{2}{*}{**Merkel Cells**} & **Meissner’s** \\  & **Corpuscle** & & & **Corpuscle** \\ \hline Stimuli Frequency (Hz) & 40–500+ & 100–500+ & 0.4–3 & 3–40 \\ Stimuli Type & High Frequency Vibration & Sustained Downward Pressure; Skin Slip; Tangential Force & Spatial deformation; Sustained pressure; Texture & Low Frequency Vibration \\ \hline \hline \end{tabular}
\end{table}
Table 2: The stimuli that the four mechanoreceptors sensed [18].  
Figure 3: The three sequences of 12 materials. (**a**) The sequences of a random sample; (**b**) the average sequences of all samples.