## 4 Results
### Sensor-Based Detection of Motor Patterns and Stereotypical Behaviors

Table 2: _Cont._integration offers a promising approach for early screening and continuous tracking of ASD-related motor behaviors, facilitating more timely interventions and personalized care. In a related vein, [PERSON] et al. (2019) [5] explored how guided play environments embedded with digital sensors could capture stereotypical play behaviors indicative of ASD. This study utilized a setup in which children engaged in structured play activities designed to encourage both typical and atypical behaviors, with sensors embedded to track these interactions. Several innovative smart toys equipped with wireless sensors have been designed to monitor and detect stereotypical body movements during play. These devices often incorporate technologies such as accelerometers and video cameras to achieve precise tracking and analysis. The data collected from these play sessions provided insight into behavioral patterns, particularly repetitive and restricted play tendencies often observed in children with ASD. Using comparative analysis between children with ASD and neurotypical peers, the researchers achieved an accuracy rate of 91% in detecting subtle behavioral differences. This approach not only demonstrated high diagnostic value but also highlighted guided play as a potentially therapeutic interaction, as it promotes social engagement while enabling objective measurement of behavioral cues. The study concluded that guided play environments equipped with digital sensing offer dual benefits: they serve as a diagnostic tool for early ASD detection and may also support therapeutic interventions by encouraging naturalistic social interaction. [PERSON] et al. (2009) [7] focused on vocal analysis as a non-intrusive means of identifying ASD-related speech patterns. The researchers developed a machine learning model to analyze vocalizations by decomposing them into phone-like units. Advanced algorithms extract features from these units to identify ASD-specific vocal characteristics, such as atypical pitch, rhythm, or sound production patterns. The extracted features are processed using machine learning models, which classify the data and determine the likelihood of ASD presence with high sensitivity and specificity. By comparing the vocal patterns in children with ASD against those in neurotypical children, the study was able to detect distinct characteristics in speech prosody (rhythm, intonation, pitch, stress, and tempo of spoken language) and articulation. This analysis yielded a high accuracy rate of 89%, indicating that vocalization decomposition could effectively distinguish ASD-specific vocal traits. The study emphasized the feasibility of using vocal data for ASD screening, noting that this method's practicality lies in its adaptability across various settings, from clinical environments to at-home applications. The findings support vocal analysis as an accessible and effective tool for early ASD detection, especially when other diagnostic methodologies are unavailable or impractical. Summarizing, these studies underscore the potential of wearable sensors, guided play environments, and vocal analysis in advancing ASD detection and monitoring. Each approach leverages unique data modalities--motor patterns, play interactions, and vocal characteristics--offering complementary perspectives on ASD's behavioral markers. By facilitating early and accurate detection, these technological approaches pave the way for personalized support and enhanced quality of life for individuals with ASD and their families.