A headache and drowsiness are the most common symptoms of fatigue caused by a long duration of work using a visual display terminal (VDT). A sign of the headache generally involves placing a hand on the head, eyes, nose, or face. The recognition of these gestures is a challenging problem due to the difficulty in similar skin color of hands and face. In this paper, a method for classifying six hand over face poses, which can identify the signs of headache for the VDT workers is presented. In the proposed method, a deep learning based on a convolutional neural network (CNN) for the classification of the hand poses is applied. In addition, a class activation map (CAM) to visualize the prediction of the classification network for localization of the hand over face poses was implemented. From the experimental results, the hand poses as the signs of frontal, and unilateral headaches without the classification overfitting and data biasing errors were successfully classified. Our proposed method has achieved high accuracy recognition ratio of 98.5% for classification of the hand over face poses as the prediction of headaches. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

中文翻译：

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使用带类激活映射的卷积神经网络预测VDT工人的头痛

头痛和嗜睡是使用可视显示终端（VDT）长时间工作导致的最常见疲劳症状。头痛的迹象通常涉及将手放在头部，眼睛，鼻子或脸上。由于手和脸部的肤色相似，这些手势的识别是一个具有挑战性的问题。本文提出了一种分类六种移交面部姿势的方法，该方法可以为VDT工人识别头痛的迹象。在所提出的方法中，应用了基于卷积神经网络（CNN）的深度学习来对手势进行分类。此外，还实施了一个类别激活图（CAM），以可视化分类网络的预测，以便对移交的面部姿势进行定位。从实验结果来看 成功地将手作为额骨和单侧头痛的体征，没有分类过拟合和数据偏倚错误。我们提出的方法已经实现了98.5％的高准确率识别率，用于将交出的面部姿势分类为头痛的预测。©2020日本电气工程师学会。由Wiley Periodicals LLC发布。