(1) Background: As digital technology continues to reshape visual landscapes, understanding how design elements influence customer experience has become essential. Eye-tracking technology offers a powerful, quantitative approach to assessing visibility, aesthetics, and design components, providing unique insights into visual engagement. (2) Methods: This paper presents a systematic review of eye-tracking methodologies applied in design research. Thirty studies were selected for analysis from recognized academic databases using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. Employing the Population, Intervention, Comparison, and Outcomes (PICO) framework, this review focused on experimental studies in visual communication and new media design that utilized visual symbols for communication and leveraged new media technologies. (3) Results: The findings corroborated that eye-tracking technology offers in-depth insights into gaze patterns, visual perception, and attention, which can inform design strategies. This review shows that assessing visual designs based on eye-tracking data can enhance consumer-centered interfaces, better align with user preferences, and foster more engaged behaviors in both digital and physical environments. (4) Conclusions: This review deepens our understanding of the cognitive and emotional processes underlying visual engagement. It also suggests new avenues for integrating diverse eye-tracking metrics into design evaluation, offering practical applications for improving design strategies and advancing the field of design research.

As an emerging field of robotics, magnetic-field-controlled soft microrobot has broad application prospects for its flexibility, locomotion diversity, and remote controllability. Magnetic soft microrobots can perform multimodal locomotion under the control of a magnetic field, which may have potential applications in precision medicine. However, previous research studies mainly focus on new locomotion in a relatively ideal environment, lacking exploration on the ability of magnetic microrobot locomotion to resist external disturbances and proceed in a nonideal environment. Here, a porous silica-doped soft magnetic microrobot is constructed for enhanced stability of multimodal locomotion in the nonideal biological environment. Porous silica spheres are doped into a NdFeB–silicone elastomer base, improving adhesion properties and refining the comprehensive mechanical properties of the microrobot. Multimodal locomotions are achieved, and the influence of porous silica doping on the stability of each locomotion in a nonideal environment is explored in depth. Motions in nonideal circumstances such as climbing, loading, current rushing, wind blowing, and obstacle hindering are conducted successfully with porous silica doping. Such a stability-enhanced multimodal locomotion system can be used in biocatalysis and thrombus removal, and its prospect for precision medicine is highlighted by in vivo demonstration of multimodal locomotion with nonideal disturbance.

This study explores how natural backgrounds in home workout video interfaces influence users’ affective and neurophysiological responses, conceptualizing these backgrounds as biophilic visual elements that may enhance affective user experience. Moving beyond traditional self-report approaches, it aimed to provide neuroscience-informed evidence for the design of affect-enhancing digital fitness interfaces. A within-subjects crossover experimental design with counterbalanced order effects was employed to compare two conditions: indoor workout videos with non-biophilic backgrounds and outdoor workout videos featuring natural environments. Participants’ emotional responses were assessed using the Positive and Negative Affect Schedule (PANAS) and the State–Trait Anxiety Inventory (STAI), while neural activity was recorded using electroencephalography (EEG). In addition, machine learning models were applied to EEG-derived spectral features to classify environmental conditions. Behavioral results indicated that outdoor workout videos significantly increased positive affect and reduced negative affect compared to indoor videos, while no significant changes were observed in state anxiety. EEG-based classification successfully distinguished between indoor and outdoor conditions, with parietal gamma-band activity providing the highest discriminative performance. These findings suggest that biophilic visual environments enhance affective user experience primarily through perceptual–affective mechanisms rather than direct anxiety reduction. This study contributes to human–computer interaction research by integrating dynamic video stimuli, neurophysiological measurement, and machine learning, and advancing a neuroscience-informed framework for designing emotionally supportive digital exercise environments.