TPRO-NET: an EEG-based emotion recognition method reflecting subtle changes in emotion.

Emotion recognition based on Electroencephalogram (EEG) has been applied in various fields, including human-computer interaction and healthcare. However, for the popular Valence-Arousal-Dominance emotion model, researchers often classify the dimensions into high and low categories, which cannot reflect subtle changes in emotion. Furthermore, there are issues with the design of EEG features and the efficiency of transformer.

Intelligence Sparse Sensor Network for Automatic Early Evaluation of General Movements in Infants.

General movements (GMs) have been widely used for the early clinical evaluation of infant brain development, allowing immediate evaluation of potential development disorders and timely rehabilitation. The infants' general movements can be captured digitally, but the lack of quantitative assessment and well-trained clinical pediatricians presents an obstacle for many years to achieve wider deployment, especially in low-resource settings. There is a high potential to explore wearable sensors for movement analysis due to outstanding privacy, low cost, and easy-to-use features.

Classification and Analysis of Human Body Movement Characteristics Associated with Acrophobia Induced by Virtual Reality Scenes of Heights.

Acrophobia (fear of heights), a prevalent psychological disorder, elicits profound fear and evokes a range of adverse physiological responses in individuals when exposed to heights, which will lead to a very dangerous state for people in actual heights. In this paper, we explore the behavioral influences in terms of movements in people confronted with virtual reality scenes of extreme heights and develop an acrophobia classification model based on human movement characteristics. To this end, we used wireless miniaturized inertial navigation sensors (WMINS) network to obtain the information of limb movements in the virtual environment.

Gated Recurrent Unit Network for Psychological Stress Classification Using Electrocardiograms from Wearable Devices.

In recent years, research on human psychological stress using wearable devices has gradually attracted attention. However, the physical and psychological differences among individuals and the high cost of data collection are the main challenges for further research on this problem. In this work, our aim is to build a model to detect subjects' psychological stress in different states through electrocardiogram (ECG) signals.