Research on Aging Design of ATM Human-Machine Interface Based on Visual Features

Zhang, Jiaqi; Huang, Tianyu

doi:10.1007/978-3-031-05412-9_19

Cited by 2 publications

(6 citation statements)

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“…As discussed previously, the placement of visual perception elements within the human-computer interaction (HCI) interface takes into account the visual characteristics of cone cells. The calculation of the Z index for the AR-HUD visual elements is determined according to the following equation ( 4): (Ding, 2017;Liu & Huang, 2021;Zhang et al, 2016):…”

Section: Optimal Mathematical Model Based On Cognitive Load and Visua...mentioning

confidence: 99%

“…The size of the combined operation sequence for each component area was determined by dividing the sum of ordinal numbers by the square of the usage frequency of the component area. This calculation was performed as part of the layout encoding experiments, as detailed in the studies conducted by Liu and Huang (2021), Ding (2017), andZhang et al (2016). The calculation is performed according to the following equation ( 11): The function f c ij ( ) denotes the usage frequency of the component area c ij within the HUD module.…”

Section: Optimal Mathematical Model Based On Cognitive Load and Visua...mentioning

confidence: 99%

“…To optimize the design of the interface prototype, the objective function is formulated based on the Z-Index, denoted as F(X), which establishes the relationship between the design variables and the corresponding Z values using an equation, with the preference determined by X (Zhang et al, 2016).…”

Section: Genetic Algorithmmentioning

confidence: 99%

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Exploring Enhancement of AR-HUD Visual Interaction Design Through Application of Intelligent Algorithms

Teng

Wan

Kong

2023

International Journal of Information Technologies and Systems Approach

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This study aims to optimize the visual interaction design of AR-HUD and reduce cognitive load in complex driving situations. An immersive driving simulation incorporating eye-tracking technology was utilized to analyze objective physiological indices and measure subjective cognitive load using the NASA-TLX. Additionally, a visual cognitive load index was integrated into a BP-GA neural network model for load prediction, enabling the derivation of an optimal solution for AR-HUD design. The optimized AR-HUD interface demonstrated a significant reduction in cognitive load compared to the previous prototype. The experimental group achieved a mean total score of 25.63 on the WP scale, whereas the control group scored 43.53, indicating a remarkable improvement of 41.4%. This study presents an innovative approach to optimizing AR-HUD design, effectively reducing cognitive load in complex driving situations. The findings demonstrate the potential of the proposed algorithm to enhance user experience and performance.

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Section: Optimal Mathematical Model Based On Cognitive Load and Visua...mentioning

confidence: 99%

Section: Optimal Mathematical Model Based On Cognitive Load and Visua...mentioning

confidence: 99%

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Exploring Enhancement of AR-HUD Visual Interaction Design Through Application of Intelligent Algorithms

Teng

Wan

Kong

2023

International Journal of Information Technologies and Systems Approach

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“…Kang studied the layout optimization design of the HCI interface under multi-objective conditions based on a genetic algorithm [16]. Zhang et al proposed a visual perception field model based on the concept of field in physics, and adopted a genetic algorithm to determine the optimal HCI interface scheme based on the visual perception field [17]. Chen et al established the principle of quantifying cognitive load based on eye-movement indicators to guide interface design, and used the sparrow search algorithm to optimize its design [18].…”

Section: Introductionmentioning

confidence: 99%

“…The higher the IN of the interface, the better the operator's information processing of the interface, and the lower the ineffective cognitive load is. The definition of IN is shown in formula (17). Figure 9 shows examples of interface optimization results at different stages.…”

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confidence: 99%

Intelligent Optimization Method of Human–Computer Interaction Interface for UAV Cluster Attack Mission

Chen,

Xie,

Wang

et al. 2023

Electronics

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In modern warfare, it is often necessary for the operator to control the UAV cluster from a ground control station to perform an attack task. However, the absence of an effective method for optimizing the human–computer interface in ground control stations for UAV clusters leads to usability difficulties and heightens the probability of human errors. Hence, we propose an optimization framework for human–computer interaction interfaces within UAV ground control stations, rooted in interface-essential elements. Specifically, the interface evaluation model was formulated by combining the Salient, Effort, Expectancy, and Value (SEEV) framework with the essential factor mutation cost of the quantified interface. We employed the SEEV–ant colony algorithm to address the challenge of optimizing the interface design within this context. For a typical UAV cluster attack mission, we optimized the human–computer interaction interfaces of the three mission stages based on the proposed SEEV-AC model. We conducted extensive simulation experiments in these optimized interfaces, and used eye-movement indicators to evaluate the effectiveness of the interface optimization model. Based on the experimental results, divergence is reduced by 11.59%, and the fitness of the optimized interface is increased from 1.34 to 3.42. The results show that the proposed intelligent interface optimization method can effectively improve the interface design and reduce the operator’s workload.

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Research on Aging Design of ATM Human-Machine Interface Based on Visual Features

Cited by 2 publications

References 1 publication

Exploring Enhancement of AR-HUD Visual Interaction Design Through Application of Intelligent Algorithms

Exploring Enhancement of AR-HUD Visual Interaction Design Through Application of Intelligent Algorithms

Intelligent Optimization Method of Human–Computer Interaction Interface for UAV Cluster Attack Mission

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