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

2026-09-21

AI Summary

研究通过多模态可穿戴传感技术检测自闭症青少年在行为升级前的烦躁状态，采用预训练基础模型融合方法，有效识别个体化的早期烦躁迹象。

Abstract

Challenging behaviors including aggression, self-injury, and property destruction are observed in 68% of autistic youth and pose risks to youth and caregivers. These episodes are preceded by agitation, a rising state of distress expressed through movement, vocalization, and autonomic arousal. Its signs are subtle and individualized, and its autonomic components are invisible without instrumentation. We collected upper-body movement from inertial measurement units, physiology from a wrist-worn device, and vocalizations from lapel microphones across 30 clinician-led sessions with 15 autistic youth, paired with expert behavioral annotations. We adapt four pretrained foundation models, one per modality, project each to a shared 128-dimensional space, and fuse them into a single group model. The model detected agitation with an area under the ROC curve of 0.724 at the clinician-annotated onset (within-participant permutation p=0.0005), declining to 0.608 at 30,s before onset. Thirteen of fifteen participants were above chance. A from-scratch configuration reached only 0.58, while frozen and fine-tuned features performed comparably (0.71 and 0.72). Audio contributed most of the signal, and a watch-only configuration stayed near chance. Individualized agitation is therefore detectable, including in unannotated windows preceding the annotated onset, using foundation-model transfer with one shared model rather than one per child.

Problem

• Agitation
• Autistic Youth
• Challenging Behaviors
• Multimodal Wearable Sensing

Innovation

• multimodal wearable sensing
• foundation model transfer
• agitation detection
• autistic youth