Dataset Information

Quantitative Evaluation of Visual Aesthetics of Human-Machine Interaction Interface Layout.

ABSTRACT: The current research on human-machine interaction interface layout focused on ergonomic analysis, while the research on aesthetics and aesthetic degree calculation of interface layout was insufficient. In order to objectively evaluate the aesthetic degree of interface layout, this paper put forward an aesthetic degree evaluation method of interface design based on Kansei engineering. Firstly, the perceptual image structure of interface aesthetic degree was analyzed from the perspective of aesthetic cognition. Six aesthetic image factors affecting interface aesthetic degree, including proportion, conciseness, order, rhythm, density, and equilibrium, were extracted by factor analysis method, and the variance contribution rate of each factor was taken as the weight. Secondly, according to the six aesthetic degree indexes, the calculation system of interface aesthetic degree was constructed, and the aesthetic degree value of aesthetic image factor was calculated by the corresponding aesthetic degree evaluation mathematical formula. Then, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was used to analyze the order of aesthetic degree superiority of design schemes, and the comprehensive aesthetic degree evaluation was carried out. Finally, the aesthetic degree evaluation of human-machine interaction interface layout of the driller's console of an AC variable frequency drilling rig was taken as an example to verify that this method was helpful for designers to optimize the design scheme. The experimental results showed that the proposed method was feasible and effective compared with the method of paired comparison commonly used in psychophysics.

SUBMITTER: Deng L

PROVIDER: S-EPMC7085848 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Quantitative Evaluation of Visual Aesthetics of Human-Machine Interaction Interface Layout.

Deng Li L Wang Guohua G

Computational intelligence and neuroscience 20200310

The current research on human-machine interaction interface layout focused on ergonomic analysis, while the research on aesthetics and aesthetic degree calculation of interface layout was insufficient. In order to objectively evaluate the aesthetic degree of interface layout, this paper put forward an aesthetic degree evaluation method of interface design based on Kansei engineering. Firstly, the perceptual image structure of interface aesthetic degree was analyzed from the perspective of aesthe ...[more]

PMID: 32256556

Similar Datasets

Project description:BackgroundHuman senses have evolved to recognise sensory cues. Beyond our perception, they play an integral role in our emotional processing, learning, and interpretation. They are what help us to sculpt our everyday experiences and can be triggered by aesthetics to form the foundations of our interactions with each other and our surroundings. In terms of Human-Robot Interaction (HRI), robots have the possibility to interact with both people and environments given their senses. They can offer the attributes of human characteristics, which in turn can make the interchange with technology a more appealing and admissible experience. However, for many reasons, people still do not seem to trust and accept robots. Trust is expressed as a person's ability to accept the potential risks associated with participating alongside an entity such as a robot. Whilst trust is an important factor in building relationships with robots, the presence of uncertainties can add an additional dimension to the decision to trust a robot. In order to begin to understand how to build trust with robots and reverse the negative ideology, this paper examines the influences of aesthetic design techniques on the human ability to trust robots.MethodThis paper explores the potential that robots have unique opportunities to improve their facilities for empathy, emotion, and social awareness beyond their more cognitive functionalities. Through conducting an online questionnaire distributed globally, we explored participants ability and acceptance in trusting the Canbot U03 robot. Participants were presented with a range of visual questions which manipulated the robot's facial screen and asked whether or not they would trust the robot. A selection of questions aimed at putting participants in situations where they were required to establish whether or not to trust a robot's responses based solely on the visual appearance. We accomplished this by manipulating different design elements of the robots facial and chest screens, which influenced the human-robot interaction.ResultsWe found that certain facial aesthetics seem to be more trustworthy than others, such as a cartoon face versus a human face, and that certain visual variables (i.e., blur) afforded uncertainty more than others. Consequentially, this paper reports that participant's uncertainties of the visualisations greatly influenced their willingness to accept and trust the robot. The results of introducing certain anthropomorphic characteristics emphasised the participants embrace of the uncanny valley theory, where pushing the degree of human likeness introduced a thin line between participants accepting robots and not. By understanding what manipulation of design elements created the aesthetic effect that triggered the affective processes, this paper further enriches our knowledge of how we might design for certain emotions, feelings, and ultimately more socially acceptable and trusting robotic experiences.

Project description:Background and objectives: Many brain-computer interfaces (BCIs) for people with severe disabilities present stimuli in the visual modality with little consideration of the visual skills required for successful use. The primary objective of this tutorial is to present researchers and clinical professionals with basic information about the visual skills needed for functional use of visual BCIs, and to offer modifications that would render BCI technology more accessible for persons with vision impairments.Methods: First, we provide a background on BCIs that rely on a visual interface. We then describe the visual skills required for BCI technologies that are used for augmentative and alternative communication (AAC), as well as common eye conditions or impairments that can impact the user's performance. We summarize screening tools that can be administered by the non-eye care professional in a research or clinical setting, as well as the role of the eye care professional. Finally, we explore potential BCI design modifications to compensate for identified functional impairments. Information was generated from literature review and the clinical experience of vision experts.Results and conclusions: This in-depth description culminates in foundational information about visual skills and functional visual impairments that affect the design and use of visual interfaces for BCI technologies. The visual interface is a critical component of successful BCI systems. We can determine a BCI system for potential users with visual impairments and design BCI visual interfaces based on sound anatomical and physiological visual clinical science.Implications for RehabilitationAs brain-computer interfaces (BCIs) become possible access methods for people with severe motor impairments, it is critical that clinicians have a basic knowledge of the visual skills necessary for use of visual BCI interfaces.Rehabilitation providers must have a knowledge of objectively gathering information regarding a potential BCI user's functional visual skills.Rehabilitation providers must understand how to modify BCI visual interfaces for the potential user with visual impairments.Rehabilitation scientists should understand the visual demands of BCIs as they develop and evaluate these new access methods.

Project description:BackgroundThe aims of this study were to investigate glaucomatous morphological changes quantitatively in the visual cortex of the brain with voxel-based morphometry (VBM), a normalizing MRI technique, and to clarify the relationship between glaucomatous damage and regional changes in the visual cortex of patients with open-angle glaucoma (OAG).MethodsThirty-one patients with OAG (age: 55.9 ± 10.7, male: female = 9: 22) and 20 age-matched controls (age: 54.9 ± 9.8, male: female = 10: 10) were included in this study. The cross-sectional area (CSA) of the optic nerve was manually measured with T2-weighed MRI. Images of the visual cortex were acquired with T1-weighed 3D magnetization-prepared rapid acquisition with gradient echo (MPRAGE) sequencing, and the normalized regional visual cortex volume, i.e., gray matter density (GMD), in Brodmann areas (BA) 17, 18, and 19, was calculated with a normalizing technique based on statistic parametric mapping 8 (SPM8) analysis. We compared the regional GMD of the visual cortex in the control subjects and OAG patients. Spearman's rank correlation analysis was used to determine the relationship between optic nerve CSA and GMD in BA 17, 18, and 19.ResultsWe found that the normal and OAG patients differed significantly in optic nerve CSA (p < 0.001) and visual cortex GMD in BA 17 (p = 0.030), BA 18 (p = 0.003), and BA 19 (p = 0.005). In addition, we found a significant correlation between optic nerve CSA and visual cortex GMD in BA 19 (r = 0.33, p = 0.023), but not in BA 17 (r = 0.17, p = 0.237) or BA 18 (r = 0.24, p = 0.099).ConclusionQuantitative MRI parametric evaluation of GMD can detect glaucoma-associated anatomical atrophy of the visual cortex in BA 17, 18, and 19. Furthermore, GMD in BA 19 was significantly correlated to the damage level of the optic nerve, as well as the retina, in patients with OAG. This is the first demonstration of an association between the cortex of the brain responsible for higher-order visual function and glaucoma severity. Evaluation of the visual cortex with MRI is thus a very promising potential method for objective examination in OAG.

Project description:Our world is filled with texture. For the human visual system, this is an important source of information for assessing environmental and material properties. Indeed-and presumably for this reason-the human visual system has regions dedicated to processing textures. Despite their abundance and apparent relevance, only recently the relationships between texture features and high-level judgments have captured the interest of mainstream science, despite long-standing indications for such relationships. In this study, we explore such relationships, as these might be used to predict perceived texture qualities. This is relevant, not only from a psychological/neuroscience perspective, but also for more applied fields such as design, architecture, and the visual arts. In two separate experiments, observers judged various qualities of visual textures such as beauty, roughness, naturalness, elegance, and complexity. Based on factor analysis, we find that in both experiments, ~75% of the variability in the judgments could be explained by a two-dimensional space, with axes that are closely aligned to the beauty and roughness judgments. That a two-dimensional judgment space suffices to capture most of the variability in the perceived texture qualities suggests that observers use a relatively limited set of internal scales on which to base various judgments, including aesthetic ones. Finally, for both of these judgments, we determined the relationship with a large number of texture features computed for each of the texture stimuli. We find that the presence of lower spatial frequencies, oblique orientations, higher intensity variation, higher saturation, and redness correlates with higher beauty ratings. Features that captured image intensity and uniformity correlated with roughness ratings. Therefore, a number of computational texture features are predictive of these judgments. This suggests that perceived texture qualities-including the aesthetic appreciation-are sufficiently universal to be predicted-with reasonable accuracy-based on the computed feature content of the textures.

Dataset Information

Quantitative Evaluation of Visual Aesthetics of Human-Machine Interaction Interface Layout.

Publications

Quantitative Evaluation of Visual Aesthetics of Human-Machine Interaction Interface Layout.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets