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.

Recently, the whole world was hit by COVID‐19 pandemic that led to health emergency everywhere. During the peak of the early waves of the pandemic, medical and healthcare departments were overwhelmed by the number of COVID‐19 cases that exceeds their capacity. Therefore, new rules and techniques are urgently required to help in receiving, filtering and diagnosing patients. One of the decisive steps in the fight against COVID‐19 is the ability to detect patients early enough and selectively put them under special care. Symptoms of this disease can be observed in chest X‐rays. However, it is sometimes difficult and tricky to differentiate “only” pneumonia patients from COVID‐19 patients. Machine‐learning can be very helpful in carrying out this task. In this paper, we tackle the problem of COVID‐19 diagnostics following a data‐centric approach. For this purpose, we construct a diversified dataset of chest X‐ray images from publicly available datasets and by applying data augmentation techniques. Then, we employ a transfer learning approach based on a pre‐trained convolutional neural network (DenseNet‐169) to detect COVID‐19 in chest X‐ray images. In addition to that, we employ Gradient‐weighted Class Activation Mapping (GradCAM) to provide visual inspection and explanation of the predictions made by our deep learning model. The results were evaluated against various metrics such as sensitivity, specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV) and the confusion matrix. The resulting models has achieved an average detection accuracy close to 98.82%. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.