Driving with anger can result in hazardous driving behaviors, often arising from inadequate communication among Traffic Participants (TPs). Currently, external Human-Machine Interface (eHMI) technology provides a novel channel for enhancing communication among TPs, yet its role in regulating driving emotions remains uncertain. This study investigates the impact of eHMI technology on the regulation of driving anger. We simulated a driving scenario in which the preceding vehicle abruptly applied brakes to elicit anger from the following driver. Three eHMI formats (Symbol, Text, and Symbol + Text) positioned at the rear of the preceding vehicle were implemented to explain the sudden braking event and regulate drivers’ emotions. Objective electroencephalogram (EEG) signals and subjective assessment data were analyzed to assess the effects of eHMIs on driving anger regulation. The results indicate that driving with eHMIs exhibited significant regulatory quality of drivers’ anger. Among the three formats, Symbol + Text eHMI exerted a notable regulatory impact, while both Symbol and Text eHMI contributed to emotional regulation. This study illustrates the positive impact of eHMI on emotion regulation in traffic environments, providing valuable backing for traffic management authorities in developing guidelines and policies, and contributing to the standardization of future eHMI technologies.

At present, the research on elderly-oriented experience for mobile applications is relatively scattered, and improvements have not been explored to meet the major psychological needs of the elderly. In this study, 15 principles for the design of mobile applications for the elderly in three aspects, i.e., autonomy, competence and relatedness, have been proposed on the basis of self-determination theory, literature review and results obtained from semi-structured interviews. In order to improve an existing mobile application with respect to these three aspects, data were collected from 45 elderly participants. The results revealed that the improved mobile application effectively catered to the basic psychological needs of older users, as compared to the earlier version. Hence, the design principles identified in the study can effectively guide designers in developing elderly-oriented mobile applications to improve the user experience and the wellbeing of older adults.

Effectively regulating anger driving has become critical in ensuring road safety. The existing research lacks a feasible exploration of anger-driving regulation. This paper delves into the effect and neural mechanisms of emotional olfactory stimuli (EOS) on regulating anger driving based on EEG. First, this study designed an angry driving regulation experiment based on EOS to record EEG signals. Second, brain activation patterns under various EOS conditions are explored by analyzing functional brain networks (FBNs). Additionally, the paper analyzes dynamic alterations in anger-related characteristics to explore the intensity and persistence of regulating anger driving under different EOS. Finally, the paper studies the frequency energy of EEG changes under EOS through time-frequency analysis. The results indicate that EOS can effectively regulate a driver's anger emotions, especially with the banana odor showing superior effects. Under banana odor stimulus, synchronization between the parietal and temporal lobes significantly decreased. Notably, the regulatory effect of banana odor is optimal and exhibits sustained efficacy. The regulatory effect of banana odor on anger emotions is persistent. Furthermore, the impact of banana odor significantly reduces the distribution of high-energy activation states in the parietal lobe region. Our findings provide new insights into the dynamic characterization of functional connectivity during anger-driving regulation and demonstrate the potential of using EOS as a reliable tool for regulating angry driving.

Detecting driver fatigue is critical for ensuring traffic safety. Electroencephalography (EEG) is the golden standard for brain activity measurement and is considered a good indicator of detecting driver fatigue. However, the current driver fatigue detection algorithm has limitations in mining and fusing the spatiotemporal characteristics of EEG signals. In this paper, we propose a multi-branch deep learning network named spatio-temporal fusion network with brain region partitioning strategy (STFN-BRPS) to improve the accuracy and robustness of driver fatigue recognition. Initially, we develop a recurrent multi-scale convolution module (RMSCM) comprising a multi-scale convolution sub-module, a CNN-Bi-LSTM sub-module, and a residual structure branch. RMSCM effectively extracts highly discriminative long short-term temporal feature information. Secondly, we propose a dynamic graph convolution module and a spatial graph edges’ importance weight assignment method based on brain region partitioning strategy, which can acquire intrinsic spatial feature information between electrodes. Thirdly, we design a feature fusion module (FFM) that utilizes channel attention to fuse long short-term temporal and spatial features. FFM learns and prioritizes the significance and relevance of each channel in the fused features. Finally, the fused spatio-temporal features are passed into the classification module to obtain the predicted driver fatigue state. Extensive comparison and ablation studies are conducted on EEG signals collected from real-world driving scenarios. The results demonstrate that the proposed STFN-BRPS model delivers superior classification performance compared to the mainstream methods. This study establishes a benchmark for EEG-based driver fatigue detection and related deep-learning modeling work.

We developed an intelligent shadow play system (ShadowTouch) based on multi-dimensional interactive perception technologies to let user interact with the shadow play figure naturally. ShadowTouch included a shadow play hardware subsystem and a multi-dimensional interactive software subsystem. In shadow play hardware subsystem, each joint of the shadow play figure was actuated by the connecting rod which was rotated through one steering gear. In multi-dimensional interactive software subsystem, a somatosensory interaction module synchronized motions between shadow play figure and human body based on skeleton point detection, and an automatic choreography module generated the motions automatically for the shadow play figure based on musical emotion recognition. The proposed system improved the immersive interaction experience and let users feel involved during the performance of shadow play, and the motions of shadow play figure (steering gear motion angles) could be saved digitally to further be applied for the reactivation and inheritance of traditional culture.

Music can trigger human emotion. This is a psychophysiological process. Therefore, using psychophysiological characteristics could be a way to understand individual music emotional experience. In this study, we explore a new method of personal music emotion recognition based on human physiological characteristics. First, we build up a database of features based on emotions related to music and a database based on physiological signals derived from music listening including EDA, PPG, SKT, RSP, and PD variation information. Then linear regression, ridge regression, support vector machines with three different kernels, decision trees, k -nearest neighbors, multi-layer perceptron, and Nu support vector regression (NuSVR) are used to recognize music emotions via a data synthesis of music features and human physiological features. NuSVR outperforms the other methods. The correlation coefficient values are 0.7347 for arousal and 0.7902 for valence, while the mean squared errors are 0.023 23 for arousal and 0.014 85 for valence. Finally, we compare the different data sets and find that the data set with all the features (music features and all physiological features) has the best performance in modeling. The correlation coefficient values are 0.6499 for arousal and 0.7735 for valence, while the mean squared errors are 0.029 32 for arousal and 0.015 76 for valence. We provide an effective way to recognize personal music emotional experience, and the study can be applied to personalized music recommendation.