Kennedy, Eilis, Lemoniatis, Emilios and Charalambous, Gabriella (2026) Effectiveness and Usability of Artificial Intelligence and/or Machine Learning Enabled Wrist and Ankle Wearables for Physiological and Behavioral Monitoring in Children and Adolescents With Autism Spectrum Disorder: A Systematic Review. Journal of Autism and Developmental Disorders . ISSN 0162-3257 (Print) 1573-3432 (Online)

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Abstract

Purpose: This review evaluates the performance, feasibility, and acceptability of Artificial Intelligence (AI) and machine learning (ML)–enabled wrist- and ankle-worn wearables for monitoring physiological and behavioral states that help predict difficulties in arousal regulation, emotion modulation, and behavioral control—often manifesting as aggression or self-injury in autistic children and adolescents. Method: A systematic search of peer-reviewed literature was conducted to May 2025 following PRISMA 2020 guidelines and a preregistered PROSPERO protocol (CRD420251068317). Participant characteristics, sensor modalities, study settings, AI/ML approaches, outcome definitions, and feasibility and acceptability measures were examined. Findings were synthesized narratively due to study heterogeneity. Results: Preliminary proof-of-concept data (n = 246; 170 autistic) suggest that AI-enabled models have the potential to anticipate aggression 1–3 min before onset (AUC 0.80–0.87) in specialized psychiatric settings. Under controlled conditions, self-injury and motor stereotypies were detected with 86–96% recall (up to 99% in initial personalized models). Emotional or sensory states were classified with 83–90% accuracy in laboratory contexts. Wrist-worn devices were generally well tolerated. Conclusion: AI-enabled wrist and ankle wearables demonstrate feasibility for anticipating behavioral escalation and detecting emotional states. Short advance-warning windows may support proactive intervention. Although current studies are necessarily small and exploratory, reflecting the early developmental stage of AI-enabled wearable technologies, they collectively provide proof-of-concept accuracy. These preliminary data justify continued research into proactive, sensor-supported behavioral monitoring in autism. Larger, longitudinal, co-designed studies in naturalistic settings are now required to establish reliability, safety, and clinical utility.

Item Type:	Article
Uncontrolled Keywords:	Aggression prediction; Artificial intelligence; Emotion detection; Machine learning; Systematic review; Wearable biosensors; Research & Development Unit
Subjects:	Children, Young People and Developmental Pyschology > Adolescents- Psychology Children, Young People and Developmental Pyschology > Child Care Children, Young People and Developmental Pyschology > Emotions Communication (incl. disorders of) > Autism Disabilities & Disorders (mental & physical) > Behaviour Disorders
Department/People:	Children, Young Adult and Family Services
URI:	https://repository.tavistockandportman.ac.uk/id/eprint/3087

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