Eyes-free interaction has gained attention for its potential to reduce fatigue and visually induced motion sickness. However, the absence of visual cues in eyes-free conditions imposes greater demands on users’ cognitive abilities during target acquisition. In this study, we employed quantitative analysis to compare the impact of the relative position (up, down, left, right) and the number of options (2, 3, 4) of the eyes-free pie menu on usability. We also analyzed the behavioral characteristics exhibited by users during the target acquisition task. The results indicate an interaction between the interface position and the number of options in relation to error rate and response duration, whereas subjective comfort is only influenced by interface position. Meanwhile, users’ behavioral characteristics also show differences. Based on those, we propose design and optimization recommendations for the eyes-free pie menu. Our study provides valuable design references for eyes-free interfaces in VR environments.

The icon design of Chinese traditional cultural app is different from that of common app in terms of graphics, semantics and abstraction, which would reduce cognitive efficiency of human-computer interaction (HMI). Based on eye-tracking technology, this paper qualitatively and quantitatively analyzes the cognitive efficiency of functional icons of traditional cultural app. According to the experimental results, we analyzed the icon style design problems in the app interface, built a recommended chart for the color usage of icons under the Practical Color Co-ordinate System (PCCS), and proposed an innovative design strategy. The results showed that icons with a combination of text and graphics are more suitable for traditional culture app. In addition, the optimal matching scheme of color matching, saturation value and display polarity preference in the interface is also obtained through experiments. This paper provide a reference basis for the research on the interface design of traditional cultural app.

Digital experience and bare-hand interaction (BHI) technique support users to engage with cultural heritage (CH) proactively. Humans perceive by following an interactive process, a fact that is particularly true in relation to the understanding, analysis and cognition of CH. We develop a BHI-based digital reconstruction and interactive narrative system for the central pagoda in Yungang Grottoes to enhance public accessibility. Furthermore, we devise an evaluation method for virtual heritage experiences, measuring cognitive load and usability using five metrics: brain region HbO2 data, NASA-TLX scores, performance, user satisfaction, and immersion. Results demonstrate that employing specific narrative strategies and BHI models for CH digital experiences effectively reduce cognitive load, consequently improving system usability and immersion. The subjective-objective combination of user experience evaluation method is universal and can effectively optimize the design strategies, which brings good insights and references for CH digital experience design and human-computer interaction research.

Color is an important attribute of human-perceived human-machine interface (HMI), and it is unclear which color combinations produce the best performance and user experience in visual search tasks. Taking the metro electronic guide screen (MEGS) as an example, the eye-tracking (E-T) device is used to record the pupil diameter and search time, and the Likert scale is used to evaluate the satisfaction and usability, and the influence of the four factors of background hue, saturation, brightness and text color in HMI on user search efficiency and user experience is discussed in depth. The results show that the recognition efficiency is best when the background hue is medium brightness, medium saturation green with white text color. Meanwhile, the results of user satisfaction and ease of use are basically consistent with the objective experimental results, and there is a certain correlation between the subjective preference of color combination and visual performance.

Research indicates that a significant number of individuals are in a suboptimal auditory health state , yet their auditory function can potentially be improved through auditory training. To raise awareness of auditory health issues, auditory training apps should provide effective yet accessible training methods alongside engaging mechanisms that motivate users to adopt and sustain auditory training habits, ultimately facilitating self-directed auditory health management. Current auditory training apps overlook the importance of user needs and motivation, as well as the relationship between the two, leading to low engagement and retention rates. We document the specific needs of both normal-hearing and mildly hearing-impaired users and analyze physiological data collected during auditory training tasks. Through lab experiment study, we provide quantitative evidence supporting the effectiveness of the auditory training method used in this study. The findings indicate that audiovisual-based auditory training contributes to improved auditory performance. Building on these insights, we develop an auditory training app prototype that integrates gamification and narrative design into the auditory training app prototype, and examine their impact on user motivation and engagement. Furthermore, based on the results, we propose design recommendations for future auditory training app development, emphasizing the need to align training effectiveness with user motivation and engagement strategies.

The color combination of the text background in digital reading can significantly impact reading efficiency, and prolonged digital reading can affect the efficiency of text recognition. Text background color combinations designed for the display characteristics of digital devices can make users’ reading more efficient. This study investigated the effects of five factors - hue, saturation, brightness, text color and background texture - on text recognition efficiency and text legibility in digital reading, using eye-tracking technology and the E-prime psychology experimental operating platform. Pupil diameter was recorded using eye-tracking technology, and correctness and response time were recorded on the E-prime psychological operating platform, with subjective assessment of text legibility on a Likert scale. The results showed that the green-black combination had better legibility and higher recognition efficiency among light background and dark text combinations. And the peach-light grey combination performed the worst. As for the dark background and light text combinations, the green-white combination showed better legibility and recognition efficiency. Meanwhile, the background texture did not significantly affect reading efficiency in the experiment. This study investigated the factors influencing digital reading interfaces regarding recognition efficiency and legibility, which provided a reference for the design of digital reading interactive interfaces and had practical implications for reducing recognition efficiency decline due to prolonged reading.

To examine the cognitive gender differences of color combinations in the visual system in the digital visualization interface, we simulate six common driving scenarios with a head-up display (HUD) and process adaptive and multicolor coding forms, and then analyze the cognitive patterns and visual characteristics of HUD color combinations by men and women through eye-tracking experiments based on quantitative color extraction. The achieved results reveal that there exist gender cognitive differences in users’ search performance and cognitive efficiency of HUD color combination vision. Additionally, through cognitive experiments and data analysis, we extract three cognitive evaluation latitudes of interface color combinations, namely discriminability, perceptibility, and cognitive performance, and develop a cognitive evaluation model of interface color combinations. This investigation aims to provide useful guidelines for digital visual interface designers to better meet the requirements of users of various genders, enhancing the safety and efficiency of color combination design.

This study investigated whether the audio-visual spatial design could improve and how it improved the user experience of bare-hand interaction (BHI) in virtual reality (VR), by adjusting spatial audio accordingly (no sound, audiovisual spatial congruency, audio-visual spatial in congruency). We asked 32 participants to complete the grasping task in two interaction scenarios with three audio-visual spatial conditions. We evaluated the user experience in terms of both intrinsic cognitive load and usability through four metrics: subjective cognitive load scores, HbO2 data in the corresponding brain regions, task time, and user satisfaction. The results showed that in near range interaction, although there was no significant difference between audio-visual spatial in congruency and congruency in terms of cognitive load and usability, they both significantly improved the user experience compared to the no sound group. In distant range interaction, audio-visual spatial in congruency significantly reduced the cognitive load and increased usability, thus improving the user experience.

Aesthetic quality evaluation has been an important research question in the field of user experience in product design. However, the feasibility and accuracy of using fNIRS data for product aesthetic quality evaluation are unknown. In this paper, we analyze the correlation and association between fNIRS data and aesthetic quality and designed a product aesthetic quality evaluation model to answer this question. We find that HBO2 data in the prefrontal (S19-D11), frontal (S4-D3), temporal (S3-D1), and parietal (S8-D8) regions of the brain have significant correlations and logistic relationships with high visual product aesthetic quality, whereas HBO2 data in the prefrontal (S19-D11) and parietal (S8-D8) regions of the brain have significant correlations and association relationships. These data can be used for products aesthetic quality evaluation. Importantly, the overall prediction accuracy of the model to evaluate products’ aesthetic quality is 84.1%. The model is therefore able to better distinguish and evaluate the aesthetic quality of products. This study demonstrates the feasibility of using fNIRS data to evaluate the aesthetic quality of products and shows that the product aesthetic quality evaluation model can provide an objective and accurate decision-making reference to help designers evaluate and improve the aesthetic quality of products.

Difficulty balance in the game level design is particularly important for user experience (UX). This study uses cognitive control and flow theory as a starting point, and based on the functional near-infrared spectroscopy technology, this research obtained oxygenated hemoglobin concentration (ΔHbO2) signals from three brain areas in the prefrontal cortex (PFC), namely the dorsolateral prefrontal cortex (DLPFC), the ventral prefrontal cortex (VLPFC), and the frontal area (FPA). Combined with the subjective flow state scale to explore the reaction difference between the video game players (VGPs) and the non-video game players (NVGPs) during the entire period of playing video games and the moment of game difficulty changes. The results show that VGPs and NVGPs have significant differences between flow state and HbO2 signals in different brain regions of the PFC. The players’ flow state is affected by the game difficulty. The HbO2 signals in the players’ DLPFC and FPA have a linear dependence relation with the flow state. This research proves that VGPs and NVGPs have different UX with game difficulty changes from the cognitive perspective. The results provide a reference for the future game level design about game difficulty balance to achieve a better UX in the game.

Autonomous driving systems have a rich and diverse set of sensors and collect a tremendous amount of data during their operations. This has significant implications for individual privacy and induces a new type of potential privacy risks - collateral privacy risks. It is important for the public and the developer community to be aware of the collateral privacy risk posed by current autonomous driving software systems. We performed data privacy analysis for the Apollo project, an open-source autonomous driving software system. We applied source code-based privacy auditing techniques tailored for this particular problem and produced preliminary results, although there were unresolved open issues remaining. As we performed auditing, Apollo was upgraded from version 3.0 to 3.5 with significant under-the-hood technology changes. It was a challenge to perform the analysis as the underlying software evolves and maintain a result that is up-to-date. To address this challenge, we developed and deployed a continuous source code privacy risk analysis tool to assist in the process. In this paper, we discuss our experience and lessons learned from this industrial case study.