# Exploring Cutout and Mixup for Robust Human Activity Recognition on Sensor and Skeleton Data +
## 5 Conclusions  
In this research, we explored the application of transformer models, specifically Vision Transformers (ViTs), for Human Activity Recognition (HAR) using time-series data. To address the prevalent data scarcity issue and enhance HAR model robustness, we adapted the mixup data augmentation technique for time-series data. Our experiments on four diverse HAR datasets (HHAR, MotionSense, UCI-HAR, and RealWorld) consistently demonstrated that models trained with mixup-augmented data outperform those trained on clean data alone. In addition to mixup, we implemented cutout augmentation on Graph Neural Network (GNN) models trained on skeleton-based HAR datasets. By masking random joints from the skeleton data, cutout forces the GNN models to learn activity patterns from incomplete data, improving generalization and robustness, particularly in scenarios where skeleton data may be noisy or incomplete. The application of cutout augmentation on skeleton datasets such as NTU RGB + D 60 and NTU RGB + D 120 showed noticeable performance gains, particularly in cross-subject and cross-view evaluations. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dataset Configuration**} & \multicolumn{2}{c}{**NTU RGB + D**} & \multicolumn{2}{c}{**NTU RGB + D 120**} \\ \cline{2-5}  & **XSubject** & **XView** & **XSubject** & **XView** \\ \hline STGCN [55] & 88.31\% & 94.89\% & 83.05\% & 88.45\% \\ STGCN with cutout & 88.50\% & 95.48\% & 83.25\% & 88.30\% \\ \hline STGCN++ [65] & 88.02\% & 94.93\% & 84.35\% & 88.64\% \\ STGCN++ with cutout & 88.23\% & 94.98\% & 84.16\% & 88.68\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of ST-GCN and ST-GCN++ models with and without cutout augmentation on the NTU 60 and NTU 120 datasets. Our approach of combining mixup augmentation with Vision Transformer models and cutout augmentation with GNN models for skeleton data presents a novel and effective methodology for advancing HAR. This combination leverages the strengths of both techniques: the powerful temporal dependency capture of transformers for time-series data and the spatial-temporal representation learning of GNNs for skeleton data. Together, these approaches address key challenges in HAR, such as variability in sensor data, incomplete skeleton data, and the need for robust, scalable models. Conceptualization, H.D. and J.K.; Data curation, H.D. ; Formal analysis, H.D. and J.K.; Funding acquisition, J.K.; Methodology, H.D. ; Software, H.D. ; Supervision, J.K.; Visualization, H.D. ; Writing--original draft, H.D. ; Writing--review and editing, J.K. All authors have read and agreed to the published version of the manuscript. This research was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIT) (2021R1A2C2008414), the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2024-2020-0-01789), and the Artificial Intelligence Convergence Innovation Human Resources Development (IITP-2024-RS-2023-00254592) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation). This work was supported by the Dongguk University Research Fund of 2024. Not applicable. We utilized publicly available HHAR, MotionSense, UCI-HAR, Real-World, and NTU RGB + D 60 and 120 datasets to train our models.