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arXiv Machine Learning By Nibraas Khan, Abigale Plunk, John Staubitz, Ingrid Shragge, Jordan Brooks, Suzanne Wright, Alec Brewer, James Dieffenderfer, Alper Bozkurt, Amy Weitlauf, Nilanjan Sarkar

Detecting Agitation Before Behavioral Escalation in Autistic Youth Through Multimodal Wearable Sensing

Read the original on arXiv Machine Learning →

The study reports that agitation—an early, subtle sign of distress—can be detected in autistic youth using multimodal wearable sensors. By fusing upper‑body movement, wrist‑worn physiological data, and vocalizations through pretrained foundation models, the authors achieved an AUC of 0.724 at the clinician‑annotated onset of agitation, with audio contributing the most signal. The approach works across 15 participants, showing that individualized agitation is detectable even before the onset and without needing a separate model per child.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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