## 4 Results
### EEG-Based Approaches, IoT, Smart Devices, and Serious Games  
This section examines the use of IoT and AI-powered smart devices, including wearables and advanced machine learning models, to leverage EEG data, stress monitoring, and physiological signals for identifying cognitive and emotional patterns associated with ASD. By integrating non-invasive, continuous monitoring methodologies with insights from three significant studies, it highlights the effectiveness of these tools in supporting ASD diagnosis and management in natural and practical settings. These technologies demonstrate their potential to enhance accuracy and accessibility for caregivers and clinicians, ultimately improving early detection and continuous behavioral monitoring of ASD. In a recent study, [PERSON] and [PERSON] (2025) proposed the High Energy and Reliable Sensory and Behavior Data Collection (HERSBDC) mechanism. This approach tackled challenges in Wireless Body Area Networks (WBANs) and wearable IoT (WIoT) systems for ASD behavior monitoring, including issues of mobility and time-driven data collection. The HERSBDC mechanism introduced uneven clustering, optimized cluster head (CH) selection, and multi-objective routing to enhance energy efficiency and data reliability. Compared to traditional methods like Low Energy Adaptive Cluster-Hierarchy (LEACH) and Distributed Energy-Efficient Clustering and Routing (DECR), HERSBDC improved network lifetime by up to 62.28%, delivery ratio by 15.04%, and reduced delay and routing overhead by over 50% and 32%, respectively. These results highlight HERSBDC as an effective solution for ASD sensory and behavior data collection [48]. In the study by [PERSON] et al. (2023) [34], researchers developed an energy-efficient wearable EEG device to detect neural patterns indicative of ASD. The primary aim was to optimize the energy consumption of the device without compromising the quality of EEG data needed for accurate ASD analysis. The device collected EEG signals while a machine learning model processed the data to identify ASD-related neural activity, employing algorithms that allowed for minimal energy expenditure. The study used accuracy, sensitivity, and specificity metrics to evaluate model performance, demonstrating a high accuracy rate that confirmed the reliability of the device in distinguishing ASD from neurotypical cases. The results indicated that EEG-based wearable sensors could provide accessible and practical options for continuous ASD detection. The study concluded that energy-efficient EEG devices have significant potential to make ASD monitoring more feasible for daily use and continuous monitoring, especially when regular assessments are needed over time. In the study by [PERSON] et al. (2022) [3], an IoT-based system was designed to capture and analyze movement and interaction patterns in children to detect ASD-related behaviors. The system employed a series of sensors to gather real-time data, transmitted them to cloud-based or edge computing systems via IoT networks, where they were processed through a machine learning model specifically trained to identify atypical behaviors associated with ASD. This enabled real-time data processing, remote monitoring, and diagnosis, facilitating continuous, accessible, and scalable healthcare solutions for ASD detection. The IoT framework ensured that the data could be securely shared and analyzed across different locations and devices. The methodology included rigorous cross-validation to ensure model robustness, achieving an accuracy rate of 92%, with sensitivity and specificity each exceeding 90%. These high accuracy metrics underscored the system's reliability across diverse environments, and the study concluded that the integration of IoT and AI offers a cost-effective, flexible solution for continuous ASD monitoring. The authors highlighted that such a system could operate in a wide range of settings, from clinics to homes, making it particularly beneficial for routine behavioral assessment in naturalistic environments.