Affective computing of multi-type urban public spaces to analyze emotional quality using ensemble learning-based classification of multi-sensor data.

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

SUBMITTER: Li R 

PROVIDER: S-EPMC9165821 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature