Pedestrians’ social empathy and interaction with wheelchair users: The impact of user gestures and mobility aid design in a pilot study
Bibliographic record
Abstract
Wheelchair users (WUs) experience various accessibility challenges in public spaces, which may lead them to seek assistance from pedestrians in difficult situations. In this context, understanding the factors influencing pedestrians' empathy and interaction with WUs facilitates their social interactions in challenging urban situations. This study examines how WUs’ body gestures and wheelchair design characteristics (WDCs) impact pedestrian perception and interaction. A pilot cross-sectional study was conducted with 52 participants in two phases: (1) a questionnaire assessing willingness to engage with WUs exhibiting independent or help-seeking gestures, and (2) evaluating four wheelchair types—from conventional to advanced—using key semantic descriptors of appearance and social perception. Findings revealed no significant relationship between age, gender, and willingness to interact across the two gesture conditions (p > 0.05), except for a significant association between age and willingness to interact with users of advanced powered wheelchairs in the help-seeking gesture condition (p = 0.027). Also, pedestrians' willingness to interact was significantly higher when WUs exhibited help-seeking gestures compared to independence gestures (p < 0.001). WDCs influenced pedestrian perceptions more strongly when WUs displayed independence (86.3 %) than help-seeking gestures (50 %). Moreover, analysis of semantic evaluations revealed distinct perceptual dimensions for advanced manual and powered wheelchairs, with three principal components identified for each, offering valuable insights for developing wheelchairs with greater social polish. This study highlights that both WDC and user gestures significantly affect pedestrian interaction, with the masking effect of help gestures on WDCs being a key finding. Additionally, advanced WDCs signify WUs' independence, helping reduce negative social stereotypes among pedestrians.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".