用视频动作分析辅助多摄像头记录的ADHD诊断
ADHD diagnosis based on action characteristics recorded in videos using machine learning
- 通过三摄像头采集视频，提取注意力与多动冲动行为特征
- 首次构建基于动作识别网络的机器学习诊断系统
- 提出可解释的分类标准，适合临床辅助与研究使用
ADHD的诊断与治疗需求急剧上升，现有服务难以及时满足。本文提出一种基于视频动作识别的新型ADHD诊断方法，通过三摄像头录制参与者在特定测试中的行为视频，实现对注意力不集中及多动/冲动行为的自动识别与分析。主要贡献包括：1）设计并实施一项聚焦注意力与多动/冲动行为的测试，采用三摄像头同步录制；2）首次实现基于动作识别神经网络的机器学习诊断系统；3）提出分类标准，用于生成诊断结果并分析ADHD相关动作特征。该方法为客观、可重复的ADHD评估提供了新路径。
原文摘要 · Abstract (English)
Demand for ADHD diagnosis and treatment is increasing significantly and the existing services are unable to meet the demand in a timely manner. In this work, we introduce a novel action recognition method for ADHD diagnosis by identifying and analysing raw video recordings. Our main contributions include 1) designing and implementing a test focusing on the attention and hyperactivity/impulsivity of participants, recorded through three cameras; 2) implementing a novel machine learning ADHD diagnosis system based on action recognition neural networks for the first time; 3) proposing classification criteria to provide diagnosis results and analysis of ADHD action characteristics.
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