A Predictive Multimodal Framework to Alert Caregivers of Problem Behaviors for Children with ASD (PreMAC)

Autor: John E. Staubitz, Johanna L. Staubitz, Lauren Shibley, Zachary Warren, Nilanjan Sarkar, Pablo Juárez, Amy Swanson, Michelle Hopton, Marney S. Pollack, Zhaobo K. Zheng, William H. Martin, Amy S. Weitlauf
Rok vydání: 2021
Předmět:
050103 clinical psychology
Autism Spectrum Disorder
Computer science
problem behaviors
medicine.medical_treatment
Applied psychology
wearable sensor
02 engineering and technology
lcsh:Chemical technology
ASD
Biochemistry
Article
functional analysis
Analytical Chemistry
0202 electrical engineering
electronic engineering
information engineering
medicine
Humans
lcsh:TP1-1185
0501 psychology and cognitive sciences
Electrical and Electronic Engineering
Child
signal processing
affective computing
Applied behavior analysis
Affective computing
Instrumentation
Problem Behavior
multimodal data
Training set
05 social sciences
medicine.disease
Atomic and Molecular Physics
and Optics
machine learning
Caregivers
Action (philosophy)
Autism spectrum disorder
Feasibility Studies
020201 artificial intelligence & image processing
Functional analysis (psychology)
Zdroj: Sensors, Vol 21, Iss 370, p 370 (2021)
Sensors
Volume 21
Issue 2
Sensors (Basel, Switzerland)
ISSN: 1424-8220
DOI: 10.3390/s21020370
Popis: 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.
Databáze: OpenAIRE
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