多模态可穿戴传感可在自闭症青少年情绪爆发前识别焦躁状态
Detecting Agitation Before Behavioral Escalation in Autistic Youth Through Multimodal Wearable Sensing
用腕表、麦克风和动作数据在自闭症孩子情绪爆发前预警，AUC 0.724，15 个孩子共用一个模型就够。
研究针对自闭症青少年中占比 68% 的攻击、自伤等挑战性行为，在其升级前的焦躁阶段进行预警。团队在 30 次临床会话中采集 15 名自闭症青少年的上半身动作、腕部生理信号和领夹麦克风语音，并配以专家行为标注。方法将四个预训练基础模型按模态分别编码，投影到共享 128 维空间后融合为单一群体模型。模型在临床医生标注的焦躁起点处 AUC 达 0.724（置换检验 p=0.0005），起点前 30 秒降至 0.608，15 人中 13 人高于随机水平。消融显示音频贡献最大，仅用手表配置接近随机，从零训练只达 0.58。
Detecting Agitation Before Behavioral Escalation in Autistic Youth Through Multimodal Wearable Sensing
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.