## 7 Experimental Study  
In this part, an evaluation is performed to determine the most effective model for identifying stereotypical motor movements (SMMs) in autistic children. We train several temporal-based CNN models on the \"MeltdownCrisis\" dataset, which contains 623 frame sequences. Our dataset contains autistic children's SMMs acquired in uncontrolled environments and crisis situations. Body frames encompass various environmental restrictions such as luminosity, children of different sizes, occultation, etc. The networks presented in Table 3 are trained with several hyperparameter values. Our evaluation focuses on both local and global spatial-temporal features with and without data augmentation, with and without the cross-validation method. Table 4 shows the results of nine experiments on SMMs classification. Each experiment employs a unique deep learning model architecture and evaluates various data augmentation and cross-validation strategies. Experiments concentrating primarily on local features use architectures such as InceptionResNetV2, VGG16, and ResNet50. These models, which include LSTM, Dropout, and Dense layers, have validation accuracy ranging from 75% to 91.25 %. Experiment 9 has the greatest result in this area, with an accuracy of 91.25% using ResNet50 and data augmentation. Models that integrate local and global features, such as ResNet18 and EfficientNet-b0, together with Transformer layers, also perform well. With 5-fold cross-validation and data augmentation, these models attain accuracy ranging from 71.67% to 89.46%. Experiment 8, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Experiment** & **Features Type** & \begin{tabular}{c} **Data** \\ **Augmentation** \\ \end{tabular} & \begin{tabular}{c} **Cross** \\ **Validation** \\ \end{tabular} & \
\begin{tabular}{c} **Model Layers** \\ **Model** \\ \end{tabular} &
\begin{tabular}{c} **Validation** \\ **Accuracy** \\ \end{tabular} \\ \hline
1 & Local + Global & Yes & Yes &
\begin{tabular}{c} EfficientNet-b0 + Transformer-batch-16 + \\ TimeDistributed Layer + LSTM + Dense Layer \\ \end{tabular} & 71.67\% \\ \hline
2 & Local & Yes & No &
\begin{tabular}{c} InceptionResNetV2 + Flatten + Dense + \\ 2-LSTM + Dropout + Dense Layer \\ \end{tabular} & 75\% \\ \hline
3 & Local + Global & Yes & Yes &
\begin{tabular}{c} VGG16 + Transformer-batch-16 + \\ TimeDistributed Layer + LSTM + Dense Layer \\ \end{tabular} & 77.56\% \\ \hline
4 & Local + Global & Yes & Yes &
\begin{tabular}{c} ResNet50 + Transformer-batch-16 + \\ TimeDistributed Layer + LSTM + Dense Layer \\ \end{tabular} & 80.71\% \\ \hline
5 & Local & Yes & No & 2 DConv + 1-LSTM Layer + Dense Layer & 81\% \\ \hline
6 & Local & Yes & No & VGG16 + Flatten + 1-LSTM+ Dense Layer & 83\% \\ \hline
7 & Local & Yes & No &
\begin{tabular}{c} InceptionV3 + Flatten + Dense + 2-LSTM + \\ Dropout+ Dense Layer \\ \end{tabular} & 87.5\% \\ \hline
8 & Local + Global & Yes & Yes &
\begin{tabular}{c} ResNet18 + Transformer-batch-16 + \\ TimeDistributed Layer + LSTM + Dense Layer \\ \end{tabular} & 89.46\% \\ \hline
9 & Local & Yes & No &
\begin{tabular}{c} ResNet50 + Flatten + Dense + 2-LSTM + \\ Dropout + Dense Layer \\ \end{tabular} & 91.25\% \\ \hline
**Our** & **Local + Global** & **Yes** & **Yes** &
\begin{tabular}{c} **3D-ResNet + Swin-3D-b Transformer +** \\ Dense Layer with cross validation** \\ \end{tabular} & **92.00\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental study of different architecture networks. which uses ResNet18 and a Transformer layer, has the greatest accuracy (89.46%) among models that use both feature types. Overall, the results show that by combining local and global features, we achieve the maximum validation accuracy of 92.00% with our proposed model described in the Section 6.5. Our proposed architecture includes preprocessing methods such as resizing frames to 224 \(\times\) 224 pixels, standardizing pixel values to the [0, 1] range, and using augmentation techniques such as random rotation, horizontal flipping, and cropping.