# A Few-Shot Learning-Based Material Recognition Scheme Using Smartphones
## 4 Few-Shot Learning-Based Material Recognition
### Data Augmentation  
Deep learning models typically require large amounts of data for effective training. However, the process of collecting real-world data is time-consuming and expensive, posing practical challenges. Additionally, training a deep learning model with a small dataset can lead to overfitting, where the model performs well on training data but poorly generalizes to new data, ultimately reducing its overall performance. Therefore, additional data augmentation techniques are necessary to address the challenges of limited data and enhance usability for real-world applications. Data augmentation is a technique that generates new data by applying various transformations to the existing data while keeping the original labels. This study utilizes data augmentation to prevent overfitting during the training of few-shot learning models and to achieve high accuracy during testing. According to Equations (1) and (2), the unique characteristics of the contacting material allow the material to be classified by using reflected vibration signals. Observation of the collected accelerometer data revealed that when the smartphone was in contact with the same material, the vibration exhibited similar waveforms with slight variations in amplitude. Furthermore, the waveforms varied depending on whether the material vibrated along with the smartphone (e.g., desks or drawers) or remained stationary (e.g.,  
Figure 3: Representation of smartphone’s vibration signals during interaction with material. sofas or blankets). Materials prone to vibrations showed changes in the waveform when the smartphone's position was altered, whereas materials less prone to vibrations exhibited minimal changes in the waveform regardless of position. In other words, for any material, vibration data collected in contact with the same material typically exhibit similar waveforms but different amplitudes. This suggests that slightly altering the amplitude can serve as a data augmentation method applicable across different materials. Also, to examine the changes in accelerometer data caused by variations in temperature and humidity around the material, we conducted the following experiments. Accelerometer data were collected by vibrating a smartphone placed on various objects, including a Bluetooth speaker, a mouse pad, and a wooden desk. During data collection, various environmental conditions were created, such as heating or cooling the objects or increasing humidity by spraying water with a mist sprayer. The detailed experimental setup and results are presented in Table 1. After measuring and calculating metrics such as the mean, maximum, minimum, and standard deviation under different conditions, the results showed that the effects of temperature and humidity on the accelerometer data were negligible. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multirow{2}{*}{**Indicator**} & \multirow{2}{*}{**Accelerometer**} & \multicolumn{3}{c}{**Temperature (40\%)**} & \multicolumn{3}{c}{**Humidity (23\({}^{\circ}\))**} \\ \cline{5-8}  & & & \(2^{\circ}\) & \(23^{\circ}\) & \(50^{\circ}\) & \(40\%\) & \(80\%\) \\ \hline \multirow{8}{*}{ \begin{tabular}{c} Bluetooth \\ Speaker \\ \end{tabular}