###### Abstract  
Computer vision using deep learning algorithms has served numerous human activity identification applications, particularly those linked to safety and security. However, even though autistic children are frequently exposed to danger as a result of their activities, many computer vision experts have shown little interest in their safety. Several autistic children show severe challenging behaviors such as the Meltdown Crisis which is characterized by hostile behaviors and loss of control. This study aims to introduce a monitoring system capable of predicting the Meltdown Crisis condition early and alerting the children's parents or caregivers before entering more difficult settings. For this endeavor, the suggested system was constructed using a combination of a pre-trained Vision Transformer (ViT) model (Swin-3D-b) and a Residual Network (ResNet) architecture to extract robust features from video sequences to extract and learn the spatial and temporal features of the Stereotyped Motor Movements (SMMs) made by autistic children at the beginning of the Meltdown Crisis state, which is referred to as the Pre-Meltdown Crisis state. The evaluation was conducted using the MeltdownCrisis dataset, which contains realistic scenarios of autistic children's behaviors in the Pre-Meltdown Crisis state, with data from the Normal state serving as the negative class. Our proposed model achieved great classification accuracy, at 92%. a +
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conditions. For instance, behaviors may intensify when the child attempts to escape from undesirable tasks or instructions (instruction/escape) or when denied access to desired items or activities (tangible). Similarly, discomfort or pain can trigger or amplify such behaviors, particularly when the child struggles to communicate their needs effectively. Additionally, self-stimulatory activities may evolve into maladaptive behaviors if they provide reinforcement or sensory feedback that the child finds calming. Finally, some behaviors serve as a means to gain attention, whether positive or negative, from caregivers or peers [5, 6, 7]. External stimuli such as isolation, boring tasks, or overwhelming sensory inputs (e.g., loud noise) further exacerbate these behaviors, underscoring the importance of identifying and addressing their underlying triggers. Accordingly, children with severe degrees of autism are sensitive to a wide range of internal and external stimuli, exhibiting a variety of aberrant repetitive behaviors. They occasionally have a Meltdown Crisis, in which they lose control and cannot be soothed until caregiver intervention or exhaustion. In such cases, it is crucial to monitor the children's conduct and allow caregivers to intervene quickly to prevent self-harm or harm to others. On the other hand, Pre-Meltdown Crisis stereotyped behaviors are a series of high-frequency movements that indicate the commencement of the Meltdown Crisis and can be caught early to avoid Meltdown consequences [8]. For example, before a Meltdown Crisis, some autistic children show behaviors such as finger fluttering, hand flapping, covering their ears, spinning their bodies, and more [9]. In Pre-Meltdown Crisis scenarios, autistic children become uncomfortable in situations and, therefore, show several abnormal expressions such as fear, disgust, sadness, and anger. Sometimes, their expressions reflect a mixture of multiple emotions like nervous smiles [8]. Therefore, it is important to integrate facial expressions into stereotyped physical activities to analyze the children's behaviors in Pre-Meltdown Crisis. Detecting SMMs in autistic children is primarily an activity identification problem with special challenges; hence, numerous activity recognition algorithms have been investigated to determine which one may best address the issue of SMM detection. In this context, videos of activities are a sequence of images concatenated based on their temporal occurrence. Practical techniques for activity recognition take into account both spatial and temporal features [4].