## 4 Experiments  
### ARRBD Dataset  
Construction of the ARRBD dataset is a key contribution of this study. During data collection, we adopted a dual channel strategy: on the one hand, we recorded 67 life-learning routine videos of 30 children with autism in a professional rehabilitation facility; on the other hand, we integrated 586 relevant videos from publicly available datasets such as SSBD, Wei-SSBD, and AV-ASD. These publicly available datasets are sourced from online media platforms, which inherently limits their quantity due to the nature of their collection and dissemination. Consequently, the overall scale of the ARRBD is constrained, posing challenges to model performance and generalization. Specifically, the limited scale of the ARRBD may lead to overfitting, where the model learns to recognize specific patterns within the training data but fails to generalize well to new, unseen data. Moreover, the restricted diversity of the dataset may hinder the model's ability to capture the full range of behaviors and contexts related to RRBs in children with autism. To address the issue of limited data, we implemented specific data augmentation techniques. We subdivided the videos into multiple segments for more granular processing, artificially increasing both the scale and diversity of the dataset. This approach not only  
Figure 4: Prompt for interpretable reasoning by GPT-4o. expanded the number of training samples but also introduced variations in the temporal structure of the videos. Additionally, we applied speed variations to videos with shorter or longer durations. By adjusting the playback speeds of these videos, we further introduced temporal dynamics, enhancing the model's ability to learn more robust features and improving its generalization capabilities. Upon completing these processes, we ultimately extracted 780 high-quality behavior segments that met our requirements. During the data annotation phase, we drew on the behavior classification criteria from the RBS-R and the aforementioned public datasets, while also considering the frequently observed behavior types and their characteristics in the recorded videos. Based on these factors, we established a three-tiered annotation system that includes three primary categories: repetitive bodily movements, object interaction behaviors, and self-injuring behaviors, and ten corresponding subcategories. Detailed descriptions of the behaviors can be found in Table 1. Annotation was carried out independently by three researchers followed by cross-verification, and final review by an experienced rehabilitation trainer to ensure that all labels accurately captured the contents of each video segment. Figure 5 illustrates representative examples of each type of behavior. The videos collected for the ARRBD were recorded with children with autism in China, while the publicly available datasets include videos of children from diverse ethnic backgrounds around the world. This combination ensures a certain degree of diversity; however, it may also introduce biases related to the representation of different ethnic groups. The dataset may not fully capture the variations in RRBs across different ethnicities, potentially affecting the model's applicability to broader populations. To mitigate this bias, we carefully balanced the representation of different groups during the data collection and annotation processes, striving to ensure that the dual-channel collected data encompassed all types of behaviors as comprehensively as possible. Our final ARRBD dataset consists of 780 video clips across 10 categories, with a total duration of 4886.09 s and 129,375 frames. Detailed statistical information about the dataset is presented in Table 2. During the subsequent model experiments, the dataset was randomly divided into training, validation, and test sets with a ratio close to 7:2:1. This division ensures that the model can fully learn the spatiotemporal features of the data