# A Few-Shot Learning-Based Material Recognition Scheme Using Smartphones
###### Abstract  
This study proposes FSMR, a material recognition scheme designed to expand context information about locations in context recognition services. FSMR identifies the material in contact with the smartphone and determines the object based on this information to obtain location data. When the smartphone sends vibrations to the object it touches, the vibration signals change according to the unique properties of the material, and the reflected signals are measured using an accelerometer. Based on the fact that the measured sensor values have distinct characteristics for each material, deep learning techniques are applied to classify the material and determine the object. The existing research on material and object recognition using smartphone vibrations and accelerometers often requires vast amounts of training data for deep learning-based models, making it challenging to apply to real-world applications. To address this issue, this study employs few-shot learning and data augmentation to significantly reduce the amount of training data required. The evaluation results show that FSMR achieved classification accuracies of up to 72.03% and 83.63% when trained with data collected over 1 s and 5 s, respectively. Keywords:context-aware; data augmentation; deep learning; few-shot learning; material recognition; smartphone +
Footnote †: 2020: _Article_  
To deliver context-aware services based on detailed location information, material recognition techniques that distinguish the surfaces in contact with a smartphone using various sensors have been studied. [PERSON] et al. [3] proposed a method for determining whether a smartphone is in a pocket or not by utilizing the distance and light between the smartphone and nearby objects. By setting thresholds for light and distance values measured by sensors, [PERSON] et al. differentiated whether the smartphone was in a pocket, a bag, or another location. [PERSON] et al. [4] proposed SpeCam, a method that uses a smartphone camera to recognize the material and color of surfaces in contact with the smartphone. SpeCam utilizes the smartphone display and front camera, performing classification through machine learning. Technologies like these, which leverage light sensors and cameras on smartphones to identify location information, have been extensively researched to provide better services to users. However, existing methods that rely on light and distance have limitations in distinguishing various objects. These methods are influenced by the resolution and quality of cameras, observe only the exterior of nearby objects, and cannot differentiate objects with similar materials. Additionally, privacy concerns may arise with camera-based approaches. To address these issues, this study proposes a material recognition technique that identifies the material of objects in contact with a smartphone by utilizing vibrations. When a smartphone vibrates, the vibration interacts differently depending on the unique physical properties of the contacting material, such as diffraction, attenuation, and reflection, enabling the differentiation of objects with similar materials. This method is less challenging in terms of classification compared to existing camera-based methods and does not pose privacy concerns. Previous studies have also explored material recognition techniques using vibrations [5; 6], but these approaches often rely on deep learning models that require a large amount of data for training. Collecting such a volume of data is impractical for real-world users. Therefore, this study introduces FSMR (few-shot learning-based material recognition), a material recognition method for smartphones that requires minimal data by leveraging vibrations and accelerometers. FSMR employs few-shot learning and data augmentation techniques. By reducing the amount of training data needed to classify new materials not previously trained, FSMR significantly enhances usability for users. The contributions of this paper are as follows:  
1.