The selection of floors and furniture in offices can impact the interior environment and potentially influence employees’ emotions and health. This study aims to investigate the effects of floor material, furniture color, and age on design evaluation, fixation duration, and time to first fixation. Twenty-four younger adults (M = 21.9, SD = 1.75) and twenty-four older adults (M = 48.1, SD = 6.85) participated in the experiment, where they viewed four different designs: wood floor—brown furniture (WF-BF), wood floor—dark furniture (WF-DF), tile floor—brown furniture (TF-BF), and tile floor—dark furniture (TF-DF). Participants also evaluated the designs and their emotions. Results showed the main effects and interactions of floor material, furniture color, and age. WF-BF performed the best in design evaluation, fixation duration, and time to first fixation, and also scored highest in joy, admiration, fascination, and desire. Older adults favored the texture of wood, while younger adults showed better acceptance of TF-DF. This study provides insights for interior design in office environments.

Navigational voice interaction is an important factor affecting driving behavior and safety under complex road conditions. However, much of the research on navigational speech has focused on aspects such as commands and tone, and little on voice familiarity. To investigate the effects of navigation voice familiarity on driving stress and emotions, this study was conducted through simulated driving and controlled experiments. Driving stress was assessed by analyzing participants’ electrocardiogram (ECG) signals and subjective data obtained from a Driving Activity Load Index (DALI) questionnaire. Driving emotions were evaluated by participants’ facial expression data and the Self-Assessment Manikin (SAM), whereas the Post-study System Usability Questionnaire (PSSUQ) scores were collected as a complementary measure of both driving stress and emotions. We found that voice familiarity had a substantial impact on driving stress and emotions. Compared with using a familiar voice, an unfamiliar voice resulted in a 26.51% increase in participants’ ECG data and a 34.12% increase in DALI total scores. In addition, the use of an unfamiliar voice was associated with a 112.50% and 200% increase in the mean intensity value of anger and disgust among participants, respectively, as well as a decrease in their PSSUQ ratings. These findings indicate that the use of unfamiliar navigation voices in complex road conditions can exacerbate driving stress and negative emotions. This study provides valuable insights for navigational Voice User Interaction (VUI) design and ultimately contributing to driving safety.

User satisfaction serves as a crucial reference for iteratively optimizing software interface designs. This paper introduces a comprehensive measurement model of user satisfaction, employing Notability and Goodnotes for case studies. The proposed model incorporates facial recognition technology to gauge the intensity of users’ facial expressions while interacting with various functions of the target interface. Additionally, an experimental observation method is employed to gather objective data, including task completion time, task success rates, and operational procedures, alongside users’ subjective evaluations. Leveraging objective data as input and subjective ratings as output, a user satisfaction prediction model based on a PSO-BP neural network has been devised. The results demonstrate an impressive accuracy rate of 86.26%, indicating a high accuracy in subjective perception prediction. This model has proven to be effective for measuring user satisfaction and evaluating software interface usability. Moreover, this research contributes to expanding the repertoire of user interface satisfaction evaluation methods, enhancing the objectivity of measurements and surpassing the efficiency of conventional experimental evaluation techniques. The proposed model holds practical significance for software interface usability assessment and optimization design.

With the widespread application of Augmented Reality Head-Up Displays (AR-HUDs) in automotive systems, optimizing their visual warning interface design is critical for enhancing driving safety and user experience. This study investigated the effects of dynamic visual cues (static, single flash, continuous flash) and monochromatic gradient effects on drivers’ time perception and subjective evaluations (e.g., confidence, attractiveness, willingness). In a 2×3 between-subjects experiment with 162 participants, we simulated four driving scenarios and measured responses using questionnaires and semi-structured interviews. Two-way ANOVA results revealed indicated that: (1) Dynamic cues (single/continuous flash) significantly improved time perception compared to static cues, with no difference between single and continuous flashes; (2) Monochromatic gradients enhanced willingness and perceived performance; (3) Gradients improved time perception and subjective experience for static/single flash cues, while continuous flash cues performed better without gradients; (4) Significant interactions existed between cue types and gradients for interest and attractiveness. These findings highlight context-dependent optimization strategies for AR-HUD design, balancing salience reduction and situational awareness enhancement to improve driving safety and user experience.

1. The rapid advancement of autonomous driving technology is reshaping the automotive industry landscape, yet fully autonomous vehicles (SAE Level 5) capable of operating without any human interven...