Autism Spectrum Disorder (ASD) necessitates comprehensive management, addressing complex challenges in social communication, behavioral regulation, and sensory processing, for which wearable technologies offer valuable tools to monitor and support interventions. Therefore, this review explores recent advancements in wearable technology, categorizing devices based on executive function, psychomotor skills, and the behavioral/emotional/sensory domain, highlighting their potential to improve ongoing management and intervention. To ensure rigor and comprehensiveness, the review employs a PRISMA-based methodology. Specifically, literature searches were conducted across diverse databases, focusing on studies published between 2014 and 2024, to identify the most commonly used wearables in ASD research. Notably, 55.45% of the 110 devices analyzed had an undefined FDA status, 23.6% received 510(k) clearance, and only a small percentage were classified as FDA Breakthrough Devices or in the submission process. Additionally, approximately 50% of the devices utilized sensors like ECG, EEG, PPG, and EMG, highlighting their widespread use in real-time physiological monitoring. Our work comprehensively analyzes a wide array of wearable technologies, including emerging and advanced. While these technologies have the potential to transform ASD management through real-time data collection and personalized interventions, improved clinical validation and user-centered design are essential for maximizing their effectiveness and user acceptance.

Attention Deficit Hyperactivity Disorder (ADHD) diagnosis is often challenging due to subjective assessments and symptom variability, which can delay accurate detection and treatment. To address these limitations, this study introduces DETEC-ADHD, a web-based application that combines machine learning (ML) techniques with multi-source data to enhance diagnostic accuracy. Unlike traditional approaches, DETEC-ADHD primarily utilizes extensive personal, medical, and psychological information for its initial classification. DETEC-ADHD further refines diagnoses by identifying ADHD subtypes (inattentive, hyperactive, combined) through theta/beta wave ratio analysis from EEG data, offering neurophysiological insights that complement its classification process. Logistic Regression, selected for its validated accuracy and reliability, served as the ML model for the app. The case studies demonstrated DETEC-ADHD’s effectiveness, achieving 100% accuracy in children and 90% in adults. By integrating diverse data sources with real-time EEG analysis, DETEC-ADHD provides a scalable, cost-effective, and accessible solution for ADHD detection and subtype identification, addressing diagnostic challenges and supporting healthcare providers, particularly in resource-limited environments.

STEAM education integrates science, technology, engineering, arts, and mathematics to foster interdisciplinary learning and support the development of 21st-century skills. However, implementing this approach requires the development of educational resources that meaningfully connect these disciplines. Augmented Reality (AR) can contribute to this effort by enabling interactive and immersive learning experiences. This paper presents AR-GenSTEAM, a generator for creating STEAM-based serious games with AR for web and mobile platforms. The generator follows a three-stage development process comprising analysis, configuration, and generation. Through this process, users select STEAM areas, target skills, game templates, AR resources, and an export platform to generate a serious game. A case study of Enkrypto, a serious game focused on encryption and decryption, is presented to illustrate the use of AR-GenSTEAM. A hybrid evaluation was conducted to evaluate the performance of AR-GenSTEAM and students’ experiences with the AR component of the Enkrypto game. The generator successfully generated web applications in 90% of attempts and mobile applications in 80%, while students rated the AR component positively in terms of pragmatic quality, hedonic quality, and overall user experience.