引用本文:张金勇,逯海洋,邵琳琳.基于色彩聚类的老年病房色彩设计研究[J].包装工程,2024,45(12):252-259.
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基于色彩聚类的老年病房色彩设计研究
张金勇,逯海洋,邵琳琳
河北工业大学,天津 300130
摘要:
目的 基于感性工学理论,以K-Means色彩聚类为基础获取老年病房患者的色彩感性意象评价。方法 首先搜集并筛选现代医院老年病房设计案例,通过K-Means聚类方法进行色彩提取以建立老年病房色彩样本库;其次运用语义差异法量化老年病房色彩的感性意象认知,再将所得评价结果运用SPSS软件进行因子分析;最后,提取影响色彩意象评价的主因子,并探讨各感性意象与色彩样本之间的对应关系。结果 通过量化老年病房患者的色彩感性意象认知,可知老年患者更倾向于选择中高明度、暖色调的色彩作为病房空间的主要色彩。结论 该流程能获取科学、客观的色彩搭配方案,并有助于缓解老年患者的疾病压力,促进康复过程,同时也为医疗环境设计领域提供了新的思路和方法。
关键词:  医疗空间  感性工学  色彩聚类  因子分析
DOI:10.19554/j.cnki.1001-3563.2024.12.025
分类号:
基金项目:
Color Design of Geriatric Ward Based on Color Clustering
ZHANG Jinyong, LU Haiyang, SHAO Linlin
(Hebei University of Technology, Tianjin 300130, China)
Abstract:
Based on the theory of perceptual engineering, the work aims to obtain the evaluation of color perceptual images of patients in geriatric wards on the basis of K-Means color clustering. Firstly, the design cases of geriatric wards in modern hospitals were collected and screened, and the color samples of geriatric wards were established by K-Means clustering method for color extraction. Secondly, the semantic difference method was used to quantify the perceptual image cognition of color in geriatric wards, and then the factor analysis was carried out to the obtained evaluation results by SPSS software. Finally, the main factors influencing the evaluation of color images were extracted, and the correspondence between each perceptual image and color samples was discussed. By quantifying the perception of color perceptual imagery of patients in the geriatric wards, it could be seen that the elderly patients were more inclined to choose the color of medium and high brightness and warm tones as the main color of the ward space. Through this process, a scientific and objective color matching scheme can be obtained, which is helpful to alleviate the disease pressure of elderly patients, promote the rehabilitation process, and also provide new ideas and methods for the field of medical environment design.
Key words:  medical space  perceptual engineering  color clustering  factor analysis

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