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• W4281755601 startingPage "e0269176" @default.
• W4281755601 abstract "The quality of urban public spaces affects the emotional response of users; therefore, the emotional data of users can be used as indices to evaluate the quality of a space. Emotional response can be evaluated to effectively measure public space quality through affective computing and obtain evidence-based support for urban space renewal. We proposed a feasible evaluation method for multi-type urban public spaces based on multiple physiological signals and ensemble learning. We built binary, ternary, and quinary classification models based on participants’ physiological signals and self-reported emotional responses through experiments in eight public spaces of five types. Furthermore, we verified the effectiveness of the model by inputting data collected from two other public spaces. Three observations were made based on the results. First, the highest accuracies of the binary and ternary classification models were 92.59% and 91.07%, respectively. After external validation, the highest accuracies were 80.90% and 65.30%, respectively, which satisfied the preliminary requirements for evaluating the quality of actual urban spaces. However, the quinary classification model could not satisfy the preliminary requirements. Second, the average accuracy of ensemble learning was 7.59% higher than that of single classifiers. Third, reducing the number of physiological signal features and applying the synthetic minority oversampling technique to solve unbalanced data improved the evaluation ability." @default.
• W4281755601 created "2022-06-13" @default.
• W4281755601 creator A5011118787 @default.
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• W4281755601 date "2022-06-03" @default.
• W4281755601 modified "2023-09-30" @default.
• W4281755601 title "Affective computing of multi-type urban public spaces to analyze emotional quality using ensemble learning-based classification of multi-sensor data" @default.
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• W4281755601 doi "https://doi.org/10.1371/journal.pone.0269176" @default.
• W4281755601 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35657805" @default.
• W4281755601 hasPublicationYear "2022" @default.
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