A Kansei-Oriented Morphological Design Method for Industrial Cleaning Robots Integrating Extenics-Based Semantic Quantification and Eye-Tracking Analysis
In the context of Industry 4.0, user demands for industrial robots have shifted toward diversification and experience-orientation. Effectively integrating users’ affective imagery requirements into industrial-robot form design remains a critical challenge. Traditional methods rely heavily on designe...
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MDPI AG
2025-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/15/8459 |
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| author | Qingchen Li Yiqian Zhao Yajun Li Tianyu Wu |
| author_facet | Qingchen Li Yiqian Zhao Yajun Li Tianyu Wu |
| author_sort | Qingchen Li |
| collection | DOAJ |
| description | In the context of Industry 4.0, user demands for industrial robots have shifted toward diversification and experience-orientation. Effectively integrating users’ affective imagery requirements into industrial-robot form design remains a critical challenge. Traditional methods rely heavily on designers’ subjective judgments and lack objective data on user cognition. To address these limitations, this study develops a comprehensive methodology grounded in Kansei engineering that combines Extenics-based semantic analysis, eye-tracking experiments, and user imagery evaluation. First, we used web crawlers to harvest user-generated descriptors for industrial floor-cleaning robots and applied Extenics theory to quantify and filter key perceptual imagery features. Second, eye-tracking experiments captured users’ visual-attention patterns during robot observation, allowing us to identify pivotal design elements and assemble a sample repository. Finally, the semantic differential method collected users’ evaluations of these design elements, and correlation analysis mapped emotional needs onto stylistic features. Our findings reveal strong positive correlations between four core imagery preferences—“dignified,” “technological,” “agile,” and “minimalist”—and their corresponding styling elements. By integrating qualitative semantic data with quantitative eye-tracking metrics, this research provides a scientific foundation and novel insights for emotion-driven design in industrial floor-cleaning robots. |
| format | Article |
| id | doaj-art-41d7461a36974a01a0b34b7a7523eef5 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-41d7461a36974a01a0b34b7a7523eef52025-08-20T03:36:34ZengMDPI AGApplied Sciences2076-34172025-07-011515845910.3390/app15158459A Kansei-Oriented Morphological Design Method for Industrial Cleaning Robots Integrating Extenics-Based Semantic Quantification and Eye-Tracking AnalysisQingchen Li0Yiqian Zhao1Yajun Li2Tianyu Wu3School of Design, Art and Media, Nanjing University of Science and Technology, Nanjing 210014, ChinaSchool of Design, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Design, Art and Media, Nanjing University of Science and Technology, Nanjing 210014, ChinaSchool of Art, Anhui University, Hefei 230011, ChinaIn the context of Industry 4.0, user demands for industrial robots have shifted toward diversification and experience-orientation. Effectively integrating users’ affective imagery requirements into industrial-robot form design remains a critical challenge. Traditional methods rely heavily on designers’ subjective judgments and lack objective data on user cognition. To address these limitations, this study develops a comprehensive methodology grounded in Kansei engineering that combines Extenics-based semantic analysis, eye-tracking experiments, and user imagery evaluation. First, we used web crawlers to harvest user-generated descriptors for industrial floor-cleaning robots and applied Extenics theory to quantify and filter key perceptual imagery features. Second, eye-tracking experiments captured users’ visual-attention patterns during robot observation, allowing us to identify pivotal design elements and assemble a sample repository. Finally, the semantic differential method collected users’ evaluations of these design elements, and correlation analysis mapped emotional needs onto stylistic features. Our findings reveal strong positive correlations between four core imagery preferences—“dignified,” “technological,” “agile,” and “minimalist”—and their corresponding styling elements. By integrating qualitative semantic data with quantitative eye-tracking metrics, this research provides a scientific foundation and novel insights for emotion-driven design in industrial floor-cleaning robots.https://www.mdpi.com/2076-3417/15/15/8459Kansei engineeringExtenics-based semantic analysiseye-trackingindustrial cleaning robotsmorphological design |
| spellingShingle | Qingchen Li Yiqian Zhao Yajun Li Tianyu Wu A Kansei-Oriented Morphological Design Method for Industrial Cleaning Robots Integrating Extenics-Based Semantic Quantification and Eye-Tracking Analysis Applied Sciences Kansei engineering Extenics-based semantic analysis eye-tracking industrial cleaning robots morphological design |
| title | A Kansei-Oriented Morphological Design Method for Industrial Cleaning Robots Integrating Extenics-Based Semantic Quantification and Eye-Tracking Analysis |
| title_full | A Kansei-Oriented Morphological Design Method for Industrial Cleaning Robots Integrating Extenics-Based Semantic Quantification and Eye-Tracking Analysis |
| title_fullStr | A Kansei-Oriented Morphological Design Method for Industrial Cleaning Robots Integrating Extenics-Based Semantic Quantification and Eye-Tracking Analysis |
| title_full_unstemmed | A Kansei-Oriented Morphological Design Method for Industrial Cleaning Robots Integrating Extenics-Based Semantic Quantification and Eye-Tracking Analysis |
| title_short | A Kansei-Oriented Morphological Design Method for Industrial Cleaning Robots Integrating Extenics-Based Semantic Quantification and Eye-Tracking Analysis |
| title_sort | kansei oriented morphological design method for industrial cleaning robots integrating extenics based semantic quantification and eye tracking analysis |
| topic | Kansei engineering Extenics-based semantic analysis eye-tracking industrial cleaning robots morphological design |
| url | https://www.mdpi.com/2076-3417/15/15/8459 |
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