Prediction of the VDT Worker's Headache Using Convolutional Neural Network with Class Activation Mapping

Abstract

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.

Ergonomic Reliability Assessment of VDT System for Operation Design Based on Improved BPNN and HCR under Special Circumstances

“…Many different methods are applied for prediction in reliability analysis [15,16]. Neural networks are often used in VDT-related prediction and evaluation research [17,18]. Uncertainty quantification is required for reliability assessment [19,20], and the black-box structures of neural networks can accurately construct relationships between factors with uncertain characteristics.…”