Prediction of Challenging Behaviors Associated with Profound Autism in a Classroom Setting Using Wearable Sensors

TL;DR

This study demonstrates the feasibility of predicting challenging behaviors in children with profound autism in real-world classrooms using multimodal wearable sensors and machine learning, enabling proactive interventions.

Contribution

It is the first to apply multimodal wearable sensor data and foundation models for predicting challenging behaviors in actual classroom settings.

Findings

Challenging behaviors can be predicted up to 10 minutes in advance.

Achieved an AUC-ROC of 0.78 in behavior prediction.

Collected 110.7 hours of real-world classroom data from 9 participants.

Abstract

Autism Spectrum Disorder (ASD) is characterized by challenges with social interaction and communication and by restricted or repetitive patterns of thought and behavior, with significant variability in presentation. Approximately a quarter of children with ASD are classified as having profound autism, who often exhibit challenging behaviors, such as self-injurious behavior, aggression, elopement, or pica, that pose serious safety risks and disrupt learning in educational settings. Prior work has applied wearable sensors and machine learning to detect challenging behaviors, but has been largely confined to controlled laboratory environments. This work demonstrates that predicting challenging behavior episodes is feasible in a real-world special education classroom. We collected approximately 110.7 hours of labeled multimodal wearable data comprising accelerometry, electrodermal activity (EDA), and skin temperature from 9 children and young adults aged 10 to 21 years across standard classroom sessions. We fine-tuned state-of-the-art foundation models for multimodal wearable time-series analysis and show that challenging behavior episodes can be predicted up to 10 minutes in advance with an AUC-ROC of 0.78. These results establish a concrete foundation for developing proactive in-class intervention systems that enable teachers to minimize the safety risks of challenging behaviors in special education classrooms