Department of Ergonomics, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Occupational Health Research Center, Iran University of Medical Sciences, Shahid Hemmat Highway, Tehran, 1449614535, Iran.
Sci Rep. 2023 Jul 5;13(1):10843. doi: 10.1038/s41598-023-38065-1.
This study examines whether the socio-demographic factors and cognitive sign features can be used for envisaging safety signs comprehensibility using predictive machine learning (ML) techniques. This study will determine the role of different machine learning components such as feature selection and classification to determine suitable factors for safety construction signs comprehensibility. A total of 2310 participants were requested to guess the meaning of 20 construction safety signs (four items for each of the mandatory, prohibition, emergency, warning, and firefighting signs) using the open-ended method. Moreover, the participants were asked to rate the cognitive design features of each sign in terms of familiarity, concreteness, simplicity, meaningfulness, and semantic closeness on a 0-100 rating scale. Subsequently, all eight features (age, experience, education level, familiarity, concreteness, meaningfulness, semantic closeness, and simplicity) were used for classification. Furthermore, the 14 most popular supervised classifiers were implemented and evaluated for safety sign comprehensibility prediction using these eight features. Also, filter and wrapper methods were used as feature selection techniques. Results of feature selection techniques indicate that among the eight features considered in this study, familiarity, simplicity, and meaningfulness are found to be the most relevant and effective components in predicting the comprehensibility of selected safety signs. Further, when these three features are used for classification, the K-NN classifier achieves the highest classification accuracy of 94.369% followed by medium Gaussian SVM which achieves a classification accuracy of 76.075% under hold-out data division protocol. The machine learning (ML) technique was adopted as a promising approach to addressing the issue of comprehensibility, especially in terms of determining factors affecting the safety signs' comprehension. The cognitive sign features of familiarity, simplicity, and meaningfulness can provide useful information in terms of designing user-friendly safety signs.
本研究旨在探讨社会人口因素和认知符号特征是否可用于使用预测机器学习 (ML) 技术预测安全标志的可理解性。本研究将确定不同机器学习组件（如特征选择和分类）的作用，以确定安全施工标志可理解性的合适因素。共有 2310 名参与者被要求使用开放式方法猜测 20 个施工安全标志（每个强制性、禁止、紧急、警告和消防标志各有四个项目）的含义。此外，参与者被要求根据熟悉度、具体性、简单性、有意义性和语义接近度对每个标志的认知设计特征进行 0-100 评分。随后，将所有 8 个特征（年龄、经验、教育水平、熟悉度、具体性、有意义性、语义接近度和简单性）用于分类。此外，实施了 14 种最受欢迎的监督分类器，并使用这 8 个特征评估其对安全标志可理解性的预测能力。同时，还使用了过滤和包装方法作为特征选择技术。特征选择技术的结果表明，在所考虑的这 8 个特征中，熟悉度、简单性和有意义性被认为是预测所选安全标志可理解性的最相关和最有效的组件。此外，当这三个特征用于分类时，K-NN 分类器的分类准确率最高，为 94.369%，其次是中等高斯 SVM，在保留数据分割协议下的分类准确率为 76.075%。机器学习 (ML) 技术被采纳为一种有前途的方法来解决可理解性问题，特别是在确定影响安全标志理解的因素方面。熟悉度、简单性和有意义性等认知符号特征可以为设计用户友好的安全标志提供有用的信息。
