## 10 Conclusions and Future Works  
In this paper, we develop a Hybrid 3D Convolutional-Transformer model for detecting Pre-Meltdown crisis based on SMMs in autistic children using the \"MeltdownCrisis\" dataset. Our method combines local and global spatiotemporal variables to improve the detection accuracy of SMMs. The testing findings illustrate our model's robustness and usefulness, with a significant accuracy rate of **92%**. This performance outperforms numerous cutting-edge models applied to similar tasks, demonstrating the possibility of merging 3D convolutions with transformer designs for capturing complicated temporal patterns in video data. Our model's consistent performance over many validation folds, as \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Paper** & **People Nature** & **Approach** & **Dataset** & **Recognition Rate** \\ \hline
[14] & TDP & Multi-task Network & THUMOS’14 & 61.2\% \\
[2] & Autistic people & Nearest neighbor classifier & Activity Net v1.2 & 42.3\% \\  & & Nearest neighbor classifier & Autistic Dataset & 91.57\% \\  & & & 20 Actions Database & 80.0\% \\
[17] & TDP & Recurrent transformer (ReT) & 50 Action Database & 73.8\% \\  & & & 101 Action Database & 71.7\% \\  & & self-supervised learning & Kinetics-400 & 78.1\% \\
[15] & TDP & approach for video & SSV2 & 59.2\% \\  & & transformers & μ UCF-101 & 90.8\% \\  & & & HMDB-51 & 67.2\% \\
[19] & TDP & SVFormer-B UCF-101 & Kinetics-400 & 86.7\% \\  & & Evaluating arm-flapping & & 69.4\% \\  & & stereotypic behavior in autistic & & \\
[21] & Autistic people & children using computer & & \\  & & vision and machine learning & & \\  & & approaches & & \\
**Current** & **Auistic children** & **Hybrid 3D** & & \\
**Convolutional–Transformer** & **MeltdownCrisis Dataset** & **92\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparative study with state-of-the-art works. indicated by the excellent accuracy, precision, recall, and F1 scores, reinforces its suitability for real-world applications in monitoring and analyzing autistic behaviors. While the presented model shows promising results, various areas for future study can further improve its capabilities and applicability by expanding the \"MeltdownCrisis\" dataset to include additional participants and situations, increasing the model's generalizability. Moreover, we aim to develop and test a real-time monitoring system that will allow the model to be used in realistic situations such as schools or therapy sessions, providing rapid feedback and intervention. Finally, we hope to collaborate with psychologists and behavioral therapists to improve the model's interpretability and guarantee that its predictions match clinical insights, increasing its value as a diagnostic and monitoring tool. By addressing these issues, future research can build on the groundwork created in this study, propelling further advances in the automated detection of SMMs, leading to better treatment and support for people with autism. [PERSON] and [PERSON] developped the presented contributions. [PERSON] collected the dataset, and proposed the technical methodology. [PERSON] and [PERSON] performed all experiments and visualization. In addition, they wrote the original draft. [PERSON] and [PERSON] validated the proposed methodology and experiments, verified the analytical methods, and reviewed and edited the original draft. All authors have read and agreed to the published version of the manuscript. This research work was funded by Institutional Fund Projects under grant n\({}^{\circ}\) (IFPIP-696-612-1443). Ethical review and approval for this study were waived in accordance with the agreement with the ASAADA Association for Autists. Patient consent was waived for this study due to the participation of autistic children. Data collection was conducted with strict confidentiality, ensuring that all information, including images or videos, is securely protected.