Estimating Work Efficiency Using Biological Information During Computational Work with Cognitive Load

In recent years, long working hours have become a problem in Japan. One of the measures to solve this problem is to improve work efficiency, and it is essential to establish an evaluation method for this purpose. It is also said that work efficiency decreases due to an increase in cognitive load caused by long working hours. Cognitive load refers to the amount of information processed by the brain's working memory, and it is difficult to process new information or complete a task when cognitive load is high.In previous studies on cognitive load and work efficiency, biometric information is often used as a method for evaluating cognitive load, and its effectiveness has been suggested. However, at the present stage, quantitative estimation of work efficiency under cognitive load has not been studied using biometric information. In this study, we measured biometric data during a continuous addition task with the aim of estimating work efficiency under cognitive load using biometric data. As evaluation indices, we used biological information such as heart rate variability and changes in the relative concentrations of oxygenated hemoglobin (Oxy-Hb) and deoxygenated hemoglobin (Deoxy-Hb) in the prefrontal cortex. Machine learning methods were used to estimate work efficiency, and Convolutional Neural Networks (CNNs) were used to create the machine learning models. CNNs are used in a wide range of fields as machine learning methods and are also widely used in research in the fields of cognitive load and biological information.The experiment was conducted five times on four male subjects. The continuous addition task was performed on a VDT screen for 20 minutes, and the number of correct answers in 30 seconds was used as the task efficiency. Electrocardiograms and cerebral blood flow in the prefrontal cortex were measured during the experiment.Four models were created for everyone to estimate work efficiency every 30 seconds using Oxy-Hb and Deoxy-Hb in the electrocardiogram and prefrontal cerebral blood flow. The biometric data were time-series data, and CNN was used to extract time-series features. To examine the effects of electrocardiograms and cerebral blood flow on learning, we created a model with electrocardiograms alone as explanatory variables, a model with Oxy-Hb and Deoxy-Hb in cerebral blood flow as explanatory variables, and a model with electrocardiograms and Oxy-Hb and Deoxy-Hb in cerebral blood flow as explanatory variables. We also used PFI to evaluate the importance of prefrontal cortex regions on learning.The training results showed that the ECG-only model was not sufficient for estimation, while Oxy-Hb and Deoxy-Hb in the cerebral blood flow provided highly accurate estimation. The PFI values suggested that the dorsomedial prefrontal cortex and the left dorsolateral prefrontal cortex were relatively important.