Prediction of Challenging Behaviors Associated with Profound Autism in a Classroom Setting Using Wearable Sensors
A recent study explores the prediction of challenging behaviors in children with profound autism using wearable sensors in classroom settings. The research indicates that it is possible to predict such behaviors up to 10 minutes in advance, which could help in implementing proactive interventions. This work builds on previous studies by applying machine learning techniques to real-world educational environments.
- ▪Approximately a quarter of children with Autism Spectrum Disorder are classified as having profound autism.
- ▪The study collected about 110.7 hours of multimodal wearable data from 9 children and young adults aged 10 to 21 years.
- ▪Challenging behavior episodes can be predicted with an AUC-ROC of 0.78, allowing for timely interventions.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.17618 |
| Publication time | Tue, 19 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-19T04:04:57.272Z |
| Last seen | 2026-05-19T04:04:57.272Z |
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Computer Science > Artificial Intelligence arXiv:2605.17618 (cs) [Submitted on 17 May 2026] Title:Prediction of Challenging Behaviors Associated with Profound Autism in a Classroom Setting Using Wearable Sensors Authors:Yadhu Kartha, Conor Anderson, Jenny Foster, Theresa Hamlin, Johanna Lantz, Ryan Lay, Juergen Hahn, Gari D. Clifford, Hyeokhyen Kwon View a PDF of the paper titled Prediction of Challenging Behaviors Associated with Profound Autism in a Classroom Setting Using Wearable Sensors, by Yadhu Kartha and 8 other authors View PDF HTML (experimental) 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.
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