A Multi-feature and Time-aware-based Stress Evaluation Mechanism for Mental Status Adjustment

With the rapid economic development, the prominent social competition has led to increasing psychological pressure of people felt from each aspect of life. Driven by the Internet of Things and artificial intelligence, intelligent psychological pressure detection systems based on deep learning and wearable devices have acquired some good results in practical application. However, existing studies argue that the psychological stress state is influenced by the current environment. They put much attention on the momentary features but ignore the dynamic change process of mental status in the time dimension. Besides, the lack of research in the general laws of psychological stress makes it difficult to quantitatively evaluate the stress status, resulting in the inability to perceive the stress state of users effectively. Thus, this article proposes an evaluation mechanism of psychological stress for adjusting the mental status of users. Specifically, we design a multi-dimensional feature space and a time-aware feature encoder, which integrate various stress features and capture time characteristics of stress state change. Moreover, a novel mental state model is proposed, which uses the pressure features with time characteristics to evaluate the pressure stress level. This model also quantifies the internal relationship between pressure features. Last, we establish a practicable testbed to demonstrate how to evaluate and adjust mental state of users by the proposed evaluation mechanism of psychological stress.