基于多关联特征的屏幕阅读坐姿健康性判别

长期坐姿不正确将会严重危害人体健康。现有的基于计算机视觉的坐姿检测方法主要 是通过检测人体本身来判别坐姿的健康性，没有综合地考虑人体与屏幕之间的交互关系，使得对多 种不健康坐姿无法准确检测，对此提出了一种基于多关联特征的屏幕阅读坐姿健康性判别方法。从 人体自身的约束关系和人与屏幕间的约束关系这 2 个方面考虑，先检测出人体和屏幕，再根据目标 的空间方位关系综合地提取与坐姿健康关联性强的坐姿特征。接着将坐姿特征序列输入到卷积神经 网络中进行学习和分类，从而实现坐姿的健康性判别。实验结果表明，该方法可以有效地识别出屏 幕阅读时存在的多种不健康坐姿行为。与其他方法相比，具有较好地识别结果及应用价值。

Health Discrimination of Sitting Posture in Screen Reading Based on Multi-Relevant Features

Long-time incorrect sitting posture is seriously harmful to human health. The existing computer vision-based method of judging whether the sitting posture is healthy or not mainly relies on the detection of the state of the human body itself, in disregard of the interaction between the human body and the screen, resulting in a failure to accurately detect a number of unhealthy sitting postures. We proposed a method of judging the healthiness of screen-reading sitting posture based on multi-relevant features. In consideration of the constraints imposed by the human body and the binding force between the human and screen, the method extracts the features that are strongly related to the sitting posture healthiness according to the spatial orientation of the target in a comprehensive way after detecting the human body and the screen. Subsequently, the sitting posture feature sequence is input to the convolutional neural network for analysis and classification in order to judge whether it is healthy or not. The experimental results show that the method can effectively identify a variety of unhealthy sitting behaviors during screen reading. Compared with other existing methods, this method is characterized with better recognition effects and application value.