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An automotive human-machine interface design method integrating Fuzzy Kano-QFD and physiological data.

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Τίτλος: An automotive human-machine interface design method integrating Fuzzy Kano-QFD and physiological data.
Συγγραφείς: Sun X; College of Home and Art Design, Northeast Forestry University, Harbin, Heilongjiang, PR China., Guo X; College of Home and Art Design, Northeast Forestry University, Harbin, Heilongjiang, PR China., Zhang Y; College of Home and Art Design, Northeast Forestry University, Harbin, Heilongjiang, PR China., Du D; College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, Heilongjiang, PR China.
Πηγή: Ergonomics [Ergonomics] 2026 Jun; Vol. 69 (6), pp. 1068-1089. Date of Electronic Publication: 2025 May 20.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Informa Healthcare Country of Publication: England NLM ID: 0373220 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1366-5847 (Electronic) Linking ISSN: 00140139 NLM ISO Abbreviation: Ergonomics Subsets: MEDLINE
Imprint Name(s): Publication: London : Informa Healthcare
Original Publication: London, Taylor & Francis.
Ιατρικοί όροι (MeSH): Equipment Design*/methods , Fuzzy Logic* , User-Computer Interface* , Automobiles* , Man-Machine Systems* , User-Centered Design*, Humans ; Electroencephalography ; Male ; Adult ; Eye Movements ; Female ; Automobile Driving ; Young Adult ; Ergonomics
Περίληψη: Against the backdrop of escalating intelligent driving technology, the challenge for human-machine interface (HMI) design is to accurately define diverse and individualised customer requirements (CRs), as well as to ensure the stability, usability and competitive advantage of the design solution. HMI design methods that address these issues have not been thoroughly studied. To address this challenge, this study proposes a HMI design methodology that integrates fuzzy Kano features, quality function deployment (QFD) and physiological experiments (eye-tracking, electroencephalogram and electrocorticographic activity) within a human-centred design (HCD) framework. The method is robust, efficient, rapidly iterative and widely applicable in HMI design. The advantages of the proposed methodology have been demonstrated and evaluated with examples of navigational interface design to give a clear understanding. This methodology will enhance HMI design and make creating user-friendly interfaces for intelligent vehicles safer, simpler and more efficient.
Contributed Indexing: Keywords: Fuzzy Kano; HMI design; QFD; human-centred design; physiology experiment
Local Abstract: [plain-language-summary] This study proposes a design methodology that integrates Fuzzy-Kano-QFD and physiological data, verifying how to optimise the efficacy of user participation in HMI design. Second, the scope of physiological data application is extended beyond the design evaluation phase. Third, it effectively reduces the driver’s cognitive load (CL). This study examines methods to improve the stability and efficiency of HMI design, offering a reference for HMI design practitioners.
Entry Date(s): Date Created: 20250520 Date Completed: 20260518 Latest Revision: 20260518
Update Code: 20260518
DOI: 10.1080/00140139.2025.2501768
PMID: 40390521
Βάση Δεδομένων: MEDLINE
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  Data: An automotive human-machine interface design method integrating Fuzzy Kano-QFD and physiological data.
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  Data: <searchLink fieldCode="AU" term="%22Sun+X%22">Sun X</searchLink>; College of Home and Art Design, Northeast Forestry University, Harbin, Heilongjiang, PR China.<br /><searchLink fieldCode="AU" term="%22Guo+X%22">Guo X</searchLink>; College of Home and Art Design, Northeast Forestry University, Harbin, Heilongjiang, PR China.<br /><searchLink fieldCode="AU" term="%22Zhang+Y%22">Zhang Y</searchLink>; College of Home and Art Design, Northeast Forestry University, Harbin, Heilongjiang, PR China.<br /><searchLink fieldCode="AU" term="%22Du+D%22">Du D</searchLink>; College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, Heilongjiang, PR China.
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  Data: <searchLink fieldCode="JN" term="%220373220%22">Ergonomics</searchLink> [Ergonomics] 2026 Jun; Vol. 69 (6), pp. 1068-1089. <i>Date of Electronic Publication: </i>2025 May 20.
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  Data: <i>Publication</i>: London : Informa Healthcare<br /><i>Original Publication</i>: London, Taylor & Francis.
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– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Against the backdrop of escalating intelligent driving technology, the challenge for human-machine interface (HMI) design is to accurately define diverse and individualised customer requirements (CRs), as well as to ensure the stability, usability and competitive advantage of the design solution. HMI design methods that address these issues have not been thoroughly studied. To address this challenge, this study proposes a HMI design methodology that integrates fuzzy Kano features, quality function deployment (QFD) and physiological experiments (eye-tracking, electroencephalogram and electrocorticographic activity) within a human-centred design (HCD) framework. The method is robust, efficient, rapidly iterative and widely applicable in HMI design. The advantages of the proposed methodology have been demonstrated and evaluated with examples of navigational interface design to give a clear understanding. This methodology will enhance HMI design and make creating user-friendly interfaces for intelligent vehicles safer, simpler and more efficient.
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  Data: <i>Keywords: </i>Fuzzy Kano; HMI design; QFD; human-centred design; physiology experiment<br /><i>Local Abstract: </i>[plain-language-summary] This study proposes a design methodology that integrates Fuzzy-Kano-QFD and physiological data, verifying how to optimise the efficacy of user participation in HMI design. Second, the scope of physiological data application is extended beyond the design evaluation phase. Third, it effectively reduces the driver’s cognitive load (CL). This study examines methods to improve the stability and efficiency of HMI design, offering a reference for HMI design practitioners.
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  Data: 10.1080/00140139.2025.2501768
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        Value: 10.1080/00140139.2025.2501768
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        Text: English
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        StartPage: 1068
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      – SubjectFull: Electroencephalography
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      – SubjectFull: Automobiles
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      – SubjectFull: Man-Machine Systems
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      – SubjectFull: User-Centered Design
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      – TitleFull: An automotive human-machine interface design method integrating Fuzzy Kano-QFD and physiological data.
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            NameFull: Sun X
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              Text: 2026 Jun
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