ArticleJournal of autism and developmental disorders2026
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.
Article in Journal of autism and developmental disorders, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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Authors and funding
5 authors.
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Abstract
purposeThis 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.
methodA 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.
resultsPreliminary 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.
conclusionAI-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.
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Registered trials
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