# A Few-Shot Learning-Based Material Recognition Scheme Using Smartphones
## 6 Conclusions  
This study aims to develop a system that leverages smartphone vibrations and accelerometers to identify materials in contact with the device, enabling object classification for use in context-aware services. The existing research, VibePhone, which recognizes objects using vibrations and accelerometers, requires 180 s of training data for deep learning. In contrast, the proposed FSMR achieves a similar performance with much shorter data collection durations. Reducing the data collection time allows users to directly register contexts in real-world applications, facilitating personalized context-aware services. The final evaluation results show that FSMR achieved classification accuracies of 71.94%, 76.34%, 83.01%, 90.73%, 91.79%, and 91.91% when trained on data collected over 2, 5, 10, 30, 50, and 80 s, respectively, for six objects. In Section 5.2, the few-shot learning results indicate that there was a minimal performance difference between 10-shot and 15-shot configurations. This suggests diminishing returns when using more than 10 data points per class. For practical applications, collecting data for 10 s or less appears to be appropriate. Moreover, the performance for 10-way classification was relatively poor, indicating that limiting the number of classes in real-world applications may be beneficial. Additionally, performance varies depending on class combinations. When implementing an actual application, improvements to the system may be required to minimize the impact of such variations on classification accuracy. In the future, we plan to develop context-aware services utilizing location information determined by FSMR. These context-aware services are expected to provide a wider variety of services for users indoors, as more accurate location information can be utilized for the understanding of context. Additionally, by reducing the amount of data users need to collect, practicality has been enhanced, making the actual implementation of FSMR-based context-aware services feasible.  
Conceptualization, S.K.; formal analysis, S.K.; funding acquisition, S.K.; methodology, J.Y., Y.K. and S.K.; project administration, S.K.; software, J.Y. and Y.K.; supervision, S.K.; validation, J.Y. and Y.K.; visualization, J.Y.; writing--original draft, J.Y. and Y.K.; and writing--review and editing, S.K. All authors have read and agreed to the published version of the manuscript.  
This work was partly supported by the Innovative Human Resource Development for Local Intellectualization program through an Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korean government (MSIT) (IITP-2025-2020-0-01462, 50%). This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2022R1A5A8026986, 40%), and this paper was supported by a Korea Institute for Advancement of Technology (KJAT) grant funded by the Korean Government (MOTIE) (P0020536, HRD Program for Industrial Innovation, 10%).  
Not applicable.  
The data presented in this study are available in the article.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **VibePhone [6]** & & & **FSMR** & & \\ \hline Test data size (s) & 1 & 2 & 5 & 10 & 30 & 50 & 80 \\ Inference time (ms) & 19 & 22 & 26 & 31 & 53 & 74 & 115 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Response time comparison with VibePhone.