## 4 Results
### Sensor-Based Detection of Motor Patterns and Stereotypical Behaviors  
This chapter examines three studies that investigate innovative approaches for detecting stereotypical behaviors and motor patterns associated with ASD. These methodologies utilize wearable sensors, guided play interventions, and vocal analysis to enhance diagnostic precision and understanding of ASD-related characteristics. Together, these studies highlight how technology-driven approaches contribute to accurate ASD detection, continuous monitoring, and even therapeutic interventions, emphasizing the scalability and accessibility of these techniques for diverse settings. The first study conducted by [PERSON] et al. (2018) [2] examined the application of deep learning in conjunction with wearable sensors to identify stereotyped motor motions in patients with ASD. In this study, the researchers developed a convolutional neural network (CNN) model trained to recognize ASD-specific movement patterns by analyzing data collected from wearable sensors placed on the participants. The sensors gathered fine-grained motion data, capturing the frequency, intensity, and rhythm of movements characteristic of stereotypical behaviors. To validate the model, the researchers assessed sensitivity, specificity, and overall accuracy, achieving impressive results: the model demonstrated a 94% accuracy in correctly classifying ASD and non-ASD movement patterns. This high precision underscores the potential of combining wearable sensors with deep learning for non-intrusive ASD detection, suggesting a scalable method suitable for both clinical settings and home-based monitoring. The study concluded that such \begin{table}
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**First Author** & **Approach** & **Population** & **Algorithm Used** & **Key Findings** \\ \hline [PERSON] et al. (2024) [47] & Real-time facial emotion recognition system for ASD & Children with ASD & Xception (Pre-trained DCNN), Autoencoder & Achieved 95.23\% accuracy, with real-time response enabled by fog and IoT technologies. \\ \hline [PERSON] \& [PERSON] (2025) [48] & High Energy and Reliable Sensory and Behavior Data Collection (HERSBDC) for energy-efficient and reliable ASD data collection & ASD patients using IoT networks & HERSBDC algorithm (uneven clustering with multi-objective routing) & Improved network lifetime by 62.28\%, delivery ratio by 15.04\%, and reduced delay by 52.65\%. \\ \hline [PERSON] et al. (2025) [49] & Two-phase strategy for ASD diagnosis and tailored educational strategies & Toddlers with ASD & Logistic Regression, Support Vector Machine (LR-SVM) & Achieved 94\% accuracy in ASD identification and 99.29\% accuracy in educational strategy selection. \\ \hline [PERSON] et al. (2024) [50] & Combined ML, DL, and NLP approaches for ASD detection from social media text data & Twitter users (404,627 tweets, 90,000 subset for training/testing) & Decision Trees, XGB, KNN, RNN, LSTM, Bi-LSTM, BERT, BERTweet & Achieved 88\% accuracy in classifying tweets as ASD-related or non-ASD-related, demonstrating the utility of AI in early ASD detection. \\ \hline [PERSON] et al. (2024) [51] & ML and XAI approaches for ASD diagnosis with rigorous data preprocessing & Pediatric ASD patients (clinical datasets) & Neural Networks, Extreme Gradient Boosting & Achieved high accuracy, precision, and recall; XAI revealed behavioral features as key predictors, enhancing model interpretability and clinician trust. \\ \hline [PERSON] et al. (2024) [52] & AI tools for ASD diagnosis using edge computing in educational settings & Students with ASD in schools & AlexNet architecture for facial analysis & Achieved accurate, cost-effective ASD diagnosis through facial analysis, highlighting interpretive features for improving educational interventions. \\ \hline \hline \end{tabular}
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