An intelligent optimization method of exercisers' visual comfort assessment in gymnasium

Shi, Ligang; Qiu, Jinghan; Zhang, Ruinan; Li, Yuqing; Yang, Zhaojing; Qi, Xinzhu; Tao, Lulu; Li, Siying; Liu, Weiming

doi:10.1016/j.jobe.2023.107135

Cited by 5 publications

(2 citation statements)

References 61 publications

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“…Other common visual environment studies of in-house leisure activities, such as sitting still [29] and jogging [62], should be conducted and tested in the future. Physiological indicator devices can be introduced in future studies to compensate for existing limitations [63,64].…”

Section: Limitation Of Subject Group Selection and Leisure Typementioning

confidence: 99%

Visual Perception Optimization of Residential Landscape Spaces in Cold Regions Using Virtual Reality and Machine Learning

Li,

Huang,

Zhang

et al. 2024

Land

Self Cite

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The visual perception of landscape spaces between residences in cold regions is important for public health. To compensate for the existing research ignoring the cold snow season’s influence, this study selected two types of outdoor landscape space environments in non-snow and snow seasons as research objects. An eye tracker combined with a semantic differential (SD) questionnaire was used to verify the feasibility of the application of virtual reality technology, screen out the gaze characteristics in the landscape space, and reveal the design factors related to landscape visual perception. In the snow season, the spatial aspect ratio (SAR), building elevation saturation (BS), and grass proportion in the field of view (GP) showed strong correlations with the landscape visual perception scores (W). In the non-snow season, in addition to the above three factors, the roof height difference (RHD), tall-tree height (TTH), and hue contrast (HC) also markedly influenced W. The effects of factors on W were revealed in immersive virtual environment (IVE) orthogonal experiments, and the genetic algorithm (GA) and k-nearest neighbor algorithm (KNN) were combined to optimize the environmental factors. The optimized threshold ranges in the non-snow season environment were SAR: 1.82–2.15, RHD: 10.81–20.09 m, BS: 48.53–61.01, TTH: 14.18–18.29 m, GP: 0.12–0.15, and HC: 18.64–26.83. In the snow season environment, the optimized threshold ranges were SAR: 2.22–2.54, BS: 68.47–82.34, and GP: 0.1–0.14.

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Section: Limitation Of Subject Group Selection and Leisure Typementioning

confidence: 99%

Visual Perception Optimization of Residential Landscape Spaces in Cold Regions Using Virtual Reality and Machine Learning

Li,

Huang,

Zhang

et al. 2024

Land

Self Cite

View full text Add to dashboard Cite

show abstract

“…Other common visual environment studies of in-house leisure activities, such as sitting still [35]and jogging [62], should be conducted and tested in the future. Physiological indicator devices can be introduced in future studies to compensate for existing limitations [63,64].…”

Section: Limitation Of Subject Group Selection and Leisure Typementioning

confidence: 99%

Visual Perception Optimization of Residential Leisure Space in Cold Regions Using Virtual Reality and Machine Learning

Li,

Huang,

Zhang

et al. 2023

Preprint

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The visual perception of leisure spaces between residences in cold regions is important for public health. To compensate for the existing researches` lack of ignoring the cold snow season`s influence, this study selected two types of outdoor leisure space environments in non-snow and snow seasons as research objects. An eye tracker combined with a Semantic Difference (SD) questionnaire was used to verify the feasibility of the application of virtual reality technology, screen out leisure gaze characteristics, and reveal the environmental factors related to leisure visual perception. In snow season, spatial aspect ratio (SAR), building elevation saturation (BS), and grass proportion in the field of view (GP) showed strong correlation to the leisure visual perception scores (W). In non-snow season, apart from the above three factors, roof height difference (RHD), tall tree height (TTH) and color contrast (HC) also influenced W obviously. The effects of factors on W were revealed in immersive virtual en-vironment (IVE) orthogonal experiments and genetic algorithm (GA) and k-nearest neighbor algorithm (KNN) were combined to optimized the environmental factors. The optimized threshold ranges in the non-snow season environment were SAR: 1.82‒2.15, RHD: 10.81 m‒20.09 m, BS: 48.53‒61.01, TTH: 14.18 m‒18.29 m, GP: 0.12‒0.15, and HC: 18.64‒26.83. In the snow season environment, the optimized threshold ranges were SAR: 2.22‒2.54, BS: 68.47‒82.34, and GP: 0.1‒0.14.

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Multi-objective optimization for gymnasium layout in early design stage: Based on genetic algorithm and neural network

Fan,

Liu,

Tang

et al. 2024

Building and Environment

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An intelligent optimization method of exercisers' visual comfort assessment in gymnasium

Cited by 5 publications

References 61 publications

Visual Perception Optimization of Residential Landscape Spaces in Cold Regions Using Virtual Reality and Machine Learning

Visual Perception Optimization of Residential Landscape Spaces in Cold Regions Using Virtual Reality and Machine Learning

Visual Perception Optimization of Residential Leisure Space in Cold Regions Using Virtual Reality and Machine Learning

Multi-objective optimization for gymnasium layout in early design stage: Based on genetic algorithm and neural network

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