Investigation of Physiologically Interpretable Deep Neural Network Using Pulse Rate Variability in Mental Fatigue Recognition

Mental fatigue recognition by heart rate variability analysis in a photoplethysmography (PPG) device in terms of pulse rate variability (PRV) is gathering interest for stress relief and well-being applications. To realize mental fatigue recognition systems, many studies have been conducted, which showed the high accuracy of such systems evaluations. However, there are limitations in generalization due to the lack of consideration of the physiological mechanism. In this study, we developed a framework for investigating mental fatigue recognition using physiologically interpretable deep neural network (DNN) in a wrist-worn PPG device. Furthermore, we developed a novel feature extraction method that captures the temporal accumulation of arousal states to consider the perspective of cognitive resource consumption. The results suggest that the method we developed improved the mental fatigue recognition performance of the wrist-worn PPG device throughout our experiments (an accuracy of 0.90 and an F1 score of 0.91). In addition, it was observed that the method for the wrist-worn PPG device enhanced the contribution of HRV indices, known to be associated with cognitive function. The findings demonstrate that the proposed approach enhances not only accuracy but also the physiological interpretability of the model in PPG devices.