ArticleSensors (Basel, Switzerland)2021
A Predictive Multimodal Framework to Alert Caregivers of Problem Behaviors for Children with ASD (PreMAC).
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 17 citations in OpenAlex.
- Using Wearable Technology to Predict the Occurrence of Severe Behavior Problems among Neurodiverse Individuals: A Systematic Review.Perspectives on behavior science · 2026Review
- 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 · 2026Article
- Using wearable technology to evaluate the electrodermal activity of therapists assessing challenging behavior.Journal of applied behavior analysis · 2026Article
- Approaches for Predicting the Occurrence of Challenging Behaviors in Individuals with Autism Spectrum Disorder: A Narrative Review.Journal of personalized medicine · 2025Review
- Wearable sensors in paediatric neurology.Developmental medicine and child neurology · 2025Review
- Validating a Computerized Program for Supporting Visual Analysis During Functional Analysis: The Problem Behavior Multilevel Interpreter (PB.MI).Behavior analysis in practice · 2022Article
- Development and validation of a system for the prediction of challenging behaviors of people with autism spectrum disorder based on a smart wearable shirt: A mixed-methods design.Frontiers in behavioral neuroscience · 2022Article
- Bibliometric analysis of research themes and trends in childhood autism spectrum disorders from 2012 to 2021.Frontiers in public health · 2022Article
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Corrections and comments
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Authors and funding
12 authors at 2 institutions in 1 country.
Funding
Abstract
Autism Spectrum Disorder (ASD) impacts 1 in 54 children in the US. Two-thirds of children with ASD display problem behavior. If a caregiver can predict that a child is likely to engage in problem behavior, they may be able to take action to minimize that risk. Although experts in Applied Behavior Analysis can offer caregivers recognition and remediation strategies, there are limitations to the extent to which human prediction of problem behavior is possible without the assistance of technology. In this paper, we propose a machine learning-based predictive framework, PreMAC, that uses multimodal signals from precursors of problem behaviors to alert caregivers of impending problem behavior for children with ASD. A multimodal data capture platform, M2P3, was designed to collect multimodal training data for PreMAC. The development of PreMAC integrated a rapid functional analysis, the interview-informed synthesized contingency analysis (IISCA), for collection of training data. A feasibility study with seven 4 to 15-year-old children with ASD was conducted to investigate the tolerability and feasibility of the M2P3 platform and the accuracy of PreMAC. Results indicate that the M2P3 platform was well tolerated by the children and PreMAC could predict precursors of problem behaviors with high prediction accuracies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.