用视频动作识别技术首次实现低成本自闭症谱系障碍筛查
Action-Based ADHD Diagnosis in Video
- 基于视频帧级动作识别，从行为影像中提取三类核心动作特征
- 构建首个真实多模态ADHD数据集，涵盖可量化的动作模式差异
- 为医疗资源有限地区提供低成本、可推广的辅助诊断工具
注意缺陷多动障碍（ADHD）在多个领域造成显著功能损害。早期诊断与干预可显著提升患者生活质量与社会功能。近年来，机器学习方法提升了诊断的准确性和效率，但现有方法普遍依赖昂贵设备和专业人员。本文首次将基于视频的帧级动作识别网络应用于ADHD诊断，并自主采集了一个真实多模态数据集，从视频模态中提取了三类关键动作类别用于诊断分析。全部数据已提交至CNTW-NHS基金会信托机构，将由医学专家评审，后续将公开发布。
原文摘要 · Abstract (English)
Attention Deficit Hyperactivity Disorder (ADHD) causes significant impairment in various domains. Early diagnosis of ADHD and treatment could significantly improve the quality of life and functioning. Recently, machine learning methods have improved the accuracy and efficiency of the ADHD diagnosis process. However, the cost of the equipment and trained staff required by the existing methods are generally huge. Therefore, we introduce the video-based frame-level action recognition network to ADHD diagnosis for the first time. We also record a real multi-modal ADHD dataset and extract three action classes from the video modality for ADHD diagnosis. The whole process data have been reported to CNTW-NHS Foundation Trust, which would be reviewed by medical consultants/professionals and will be made public in due course.
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