1. 基于脑区社团结构的恐高程度识别模型.
- Author
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王翘秀, 王宏, 胡佛, and 化成城
- Subjects
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OCCIPITAL lobe , *VIRTUAL reality , *PERFORMANCE standards , *TALL buildings , *COMMUNITIES , *FRONTAL lobe - Abstract
With high-rise buildings emerging, objective fear of heights detection is a key step in the standardization of the aerial work industry. Taking into account virtual reality, this paper designs an aerial exposure experiment, which studies the brain neural mechanism of fear of heights reaction, and proposes the functional brain network (FBN) to detect the fear of heights .By comparing the basic topological characteristics of FBNs, the brain regions closely related to fear of heights are found through thresholding. By dividing the community structures according to the brain regions, the recognition model of fear of heights is established . The results show that the more severe the fear of heights, the more complicated the FBN. The main brain regions involved in fear of heights include frontal lobe, central area, and occipital lobe. Using these brain regions to divide the community structures, the calculation accuracy of connection strengths on fear of heights can reach (97.37±0.58)%. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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