# Exploring Cutout and Mixup for Robust Human Activity Recognition on Sensor and Skeleton Data +
## 4 Results  
Our experimental results were obtained on an NVIDIA Ampere A100 GPU with 80 gigabytes of video RAM, manufactured by NVIDIA Corporation, based in Santa Clara, CA, USA. When it comes to vision mixup methods for sensor-based data, we tested our method on four datasets, namely HHAR [61], MotionSense [62], UCI-HAR [63], and the RealWorld dataset [64]. In our experiments, we show that models trained on mixup augmented datasets produce better results than the baseline transformer-based models [22,60], as shown in Table 1 and, more succinctly, in Figure 6.  
Figure 4: RealWorld dataset with (**left**) and without (**right**) mixup (T-SNE). Figure 5: Cutout visualization on human skeleton sample. The white key-points on the right skeleton indicate where cutout has been applied to mask parts of the skeleton. The table illustrates the accuracy of the transformer model across three different training data scenarios: clean data, mixup data, and a combination of mixup and clean data. For the HHAR dataset, the model trained on clean data achieved an accuracy of 98.01%, while the model trained on mixup augmented data reached a slightly higher accuracy of 98.15%. The highest accuracy of 98.22% was observed when the model was trained on a combination of mixup and clean data, demonstrating the effectiveness of our approach in leveraging both augmented and original data to enhance model performance. Similarly, the MotionSense dataset showed a slight decrease in accuracy when trained solely on mixup data (97.98%) compared to clean data (98.17%). However, the combination of mixup and clean data resulted in an improved accuracy of 98.3%, highlighting the robustness of our method in integrating augmented data for better generalization. For the UCI-HAR dataset, the model trained on mixup data achieved a significantly higher accuracy of 94.5%, compared to 93.32% with clean data. This substantial improvement underscores the potential of mixup augmentation in scenarios with limited training data or higher variability. The combination of mixup and clean data also performed well, achieving an accuracy of 94.44%, close to the mixup-only scenario. The RealWorld dataset also benefited from mixup augmentation. The model trained on clean data achieved an accuracy of 93.94%, while the mixup augmented data resulted in an accuracy of 94.54%. The highest accuracy of 98.15%, compared to 98.22%, is the best. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Architecture** & **Training Data** & **HHAR** & **MotionSense** & **UCI** & **RealWorld** & **Average** \\ \hline \multirow{3}{*}{HART [22]} & Clean dataset & 98.01\% & 98.17\% & 93.32\% & 93.94\% & 95.86\% \\  & Mixup dataset & 98.15\% & 97.98\% & 94.50\% & 94.54\% & 96.29\% \\  & Clean + Mixup & 98.22\% & 98.30\% & 94.44\% & 94.66\% & 96.41\% \\ \hline \multirow{3}{*}{HAR-Transformer [60]} & Clean dataset & 94.99\% & 97.33\% & 89.62\% & 88.57\% & 92.63\% \\  & Mixup dataset & 96.53\% & 97.40\% & 92.26\% & 91.49\% & 94.42\% \\ \cline{1-1}  & Clean + Mixup & 97.04\% & 97.91\% & 91.75\% & 91.52\% & 94.56\% \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of transformer-based HAR model accuracy using clean, mixup, and combined training data across different datasets. Figure 6: Comparison of model accuracy across different datasets and training methods. accuracy of 94.66% was observed with the combination of mixup and clean data, reinforcing the trend seen across other datasets. On the other hand, we used the PYSKL library proposed in [65] to test cutout augmentation on the STGCN [55] and STGCN++ [65] models. Table 2 compares the performance of the ST-GCN and ST-GCN++ models with and without the application of cutout augmentation on the NTU RGB + D [66] and NTU RGB + D 120 [67] action recognition datasets, evaluated in both cross-subject (XSubject) and cross-view (XView) settings.