用可穿戴设备提前10分钟预测自闭症儿童的挑战性行为
Prediction of Challenging Behaviors Associated with Profound Autism in a Classroom Setting Using Wearable Sensors
- 基于多模态传感器数据，用大模型分析生理信号
- 提前10分钟预测行为问题，准确率AUC达0.78
- 适合特殊教育场景的实时干预系统研发
自闭症谱系障碍（ASD）以社交互动与沟通困难、以及受限或重复的行为模式为特征，其中约四分之一儿童被归类为重度自闭症，常表现出自伤、攻击、走失或异食等挑战性行为，严重威胁安全并干扰学习。以往研究多在受控实验室中使用可穿戴传感器和机器学习检测行为，本研究首次证明在真实特殊教育课堂环境中可行。我们收集了9名年龄10至21岁的学生共约110.7小时的标注多模态可穿戴数据，包括加速度计、皮电反应（EDA）和皮肤温度。通过微调先进的多模态时间序列基础模型，结果显示可提前10分钟预测挑战性行为事件，曲线下面积（AUC-ROC）达0.78。该成果为开发课堂内主动干预系统提供了坚实基础，有助于降低重度自闭症学生的安全风险。
原文摘要 · Abstract (English)
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
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