A Few-Shot Learning-Based Material Recognition Scheme Using Smartphones - Yeonju Kim; Jeonghyeon Yoon; Seungku Kim (Applied Sciences)
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.