Estimation of Intellectual Productivity Using Electrocardiograms during Computational Tasks with Cognitive Load

Long working hours have recently become a major problem in Japan. A means of eliminating long working hours is to improve factories. Thus far, understanding current work efficiency and establishing an estimation method to support it is essential. Understanding the work efficiency of individual workers allows us to propose efficiency improvement measures suited to each worker that contribute to eliminating long working hours and improving the working environment. Working long hours increases cognitive load and decreases work efficiency. With the development of information technology, tasks requiring cognitive load to capture and process information have increased, further affecting workers. Cognitive load refers to the amount of information processed by the working memory of the brain, and processing new information and completing tasks is believed to become more difficult when the cognitive load is increased. Therefore, in this study, we focused on tasks requiring cognitive load and examined a method for estimating work efficiency. In previous studies on cognitive load and work efficiency, biometric information was often used to evaluate cognitive load. Yamaguchi reported an increase in the LF and LF/HF components, indicators of sympathetic activity, and a decrease in the HF component, an indicator of parasympathetic activity, using a time-series frequency analysis of heart rate variability during continuous additive work. Mishima measured the cerebral blood flow in the prefrontal cortex during a verbal fluency task using fNIRS and reported increases in the relative values of oxygenated and total hemoglobin levels. These results suggest that biological information, such as heart rate variability and cerebral blood flow, is effective in assessing cognitive load. However, to date, there has been no research on the quantitative estimation of work efficiency under cognitive load using biometric information. In my research, I attempted to estimate work efficiency using cerebral blood flow in the prefrontal cortex. However, owing to the high cost of measurement devices and the burden of wearing them, issues remain in terms of practicality in the daily working environment. Therefore, in this study, we focused on electrocardiography as a biometric information that can be obtained more routinely and measured during a continuous addition task to establish a method to evaluate work efficiency. A machine learning method was used to estimate work efficiency, and a convolutional neural network (CNN) was used as the learning model. The continuous addition task in the experiment was performed on a VDT screen for 20 min, and the number of correct answers in 30 s was used as task efficiency. Additionally, electrocardiograms were obtained during the experiment. Learning models were created for each participant. The biometric data were time-series data, and CNN was used for feature extraction. PFI was used to evaluate the importance of each heart rate variability index.