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Record W4408274448 · doi:10.1016/j.jth.2025.102002

Integrating user perceptions of socio-emotional aspects in wheelchair design: A pilot study using Kansei Engineering

2025· article· en· W4408274448 on OpenAlexaff
Mohsen Rasoulivalajoozi, Morteza Farhoudi

Bibliographic record

VenueJournal of Transport & Health · 2025
Typearticle
Languageen
FieldPsychology
TopicColor perception and design
Canadian institutionsOntario College of Art and DesignConcordia University
Fundersnot available
KeywordsKanseiKansei engineeringPerceptionHuman–computer interactionWheelchairPsychologyApplied psychologyEngineeringComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Comfort in wheelchair use is influenced not only by ergonomic factors but also by socio-emotional aspects that shape the user's experience. This study aims to explore how socio-emotional factors can be integrated into the representational aspects of wheelchairs. A cross-sectional study was conducted with 37 wheelchair users using Kansei Engineering methods. Participants provided data on semantic and product spaces through a questionnaire covering demographics, aesthetic importance, symbolic importance, and social communication challenges. They also rated four distinct wheelchairs using Kansei words (KWs). Then, Quality Function Deployment (QFD) linked users' insights to specific wheelchair properties. Aesthetic (76%) and Symbolic (56%) importance, as well as age, were significantly associated with social communication challenges (57%) (p < 0.05). Age was significantly associated with both Aesthetic and Symbolic importance, while gender was only linked to Aesthetic importance (p < 0.05). Descriptive analysis indicated that advanced manual and powered wheelchair designs scored higher than conventional ones. Accordingly, three key components were identified for both categories, with the highest loadings of KW in each. QFD results prioritized adjustable frame design, with 8.61% for manual and 10.44% for powered models, as key to enhancing socio-emotional aspects. Beyond analyzing the dynamics of aesthetics, symbolism, and social challenges, this study uncovers users' perceptions of wheelchair design characteristics. It proposes principal components to guide designers and includes computational analysis to connect these insights with wheelchair properties, aspects often overlooked in assistive device literature. However, redesign effectiveness also hinges on understanding social factors like stereotypes, and wheelchair-related metaphors. • The aesthetic and symbolic meaning of wheelchairs, along with age, is significantly related to experiences of social communication challenges. • While age is a significant factor associated with perceptions of the aesthetic and symbolic importance of wheelchairs, gender is only linked to aesthetic importance. • The socio-emotional design of advanced wheelchairs is guided by three key components with the highest KW loadings. For AMW, these include: 1) Secure Navigation with Advanced Representation, 2) Manoeuvrability with Pleasant Social Representation, and 3) Reliability and Respectful Social Interaction. For APW, the components are: 1) Self-Reliance and Pleasant Social Representation, 2) Self-Confidence with Social Respect, and Flexibility in Mobility. 3) Together, these factors encourage designs that enhance the social and emotional polish of advanced wheelchairs. • The QFD model identifies optimizing adjustable frame design as the top priority for enhancing socio-emotional aspects in both manual and powered wheelchairs. • KE identifies essential steps for wheelchairs to meet WUs' socio-emotional needs, while redesign must also address societal perceptions and stereotypes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.216
Threshold uncertainty score0.689

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.075
GPT teacher head0.376
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2025
Admission routes1
Has abstractyes

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