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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Automatic Extraction and Detection of Characteristic Movement Patterns in Children with ADHD Based on a Convolutional Neural Network (CNN) and Acceleration Images
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Automatic Extraction and Detection of Characteristic Movement Patterns in Children with ADHD Based on a Convolutional Neural Network (CNN) and Acceleration Images

机译：基于卷积神经网络和加速度图像的多动症儿童特征性运动模式的自动提取与检测

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Attention deficit and hyperactivity disorder (ADHD) is a neurodevelopmental disorder, which is characterized by inattention, hyperactivity and impulsive behaviors. In particular, children have difficulty keeping still exhibiting increased fine and gross motor activity. This paper focuses on analyzing the data obtained from two tri-axial accelerometers (one on the wrist of the dominant arm and the other on the ankle of the dominant leg) worn during school hours by a group of 22 children (11 children with ADHD and 11 paired controls). Five of the 11 ADHD diagnosed children were not on medication during the study. The children were not explicitly instructed to perform any particular activity but followed a normal session at school alternating classes of little or moderate physical activity with intermediate breaks of more prominent physical activity. The tri-axial acceleration signals were converted into 2D acceleration images and a Convolutional Neural Network (CNN) was trained to recognize the differences between non-medicated ADHD children and their paired controls. The results show that there were statistically significant differences in the way the two groups moved for the wrist accelerometer (t-test p-value <0.05). For the ankle accelerometer statistical significance was only achieved between data from the non-medicated children in the experimental group and the control group. Using a Convolutional Neural Network (CNN) to automatically extract embedded acceleration patterns and provide an objective measure to help in the diagnosis of ADHD, an accuracy of 0.875 for the wrist sensor and an accuracy of 0.9375 for the ankle sensor was achieved.
机译：注意缺陷和多动障碍（ADHD）是一种神经发育障碍，其特征是注意力不集中，多动和冲动行为。特别是，儿童难以保持仍然表现出更高的精细运动能力和总体运动能力。本文着重分析一组22名儿童（11名患有ADHD的儿童）在学校上课时佩戴的两个三轴加速度计（一个在显着手臂的腕部上，另一个在显性腿的脚踝上）上获得的数据。 11个配对控件）。在研究过程中，有11位被ADHD确诊的儿童中有5位没有服药。没有明确指示孩子进行任何特定的活动，而是在学校交替上课后，进行少量或中等程度的体育锻炼，并在中间休息时进行更显着的体育锻炼。将三轴加速度信号转换为2D加速度图像，并训练卷积神经网络（CNN）以识别非药物ADHD儿童与其配对控件之间的差异。结果表明，两组手腕加速度计的移动方式存在统计学差异（t检验p值<0.05）。对于脚踝加速度计，统计学意义仅在实验组和对照组的非药物治疗儿童数据之间实现。使用卷积神经网络（CNN）自动提取嵌入的加速度模式并提供客观的方法来帮助诊断多动症，手腕传感器的精度为0.875，脚踝传感器的精度为0.9375。

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