Perceptual responses of (sports-)clothing-body interaction simulating pre- and post-purchase experience
Bibliographic record
Abstract
The appreciation of textile products highly depends on a satisfactory ‘feel’ in fabric-skin contact. The question arising is whether the haptic interpretation of a garment (by hand) is comparable to a feeling produced when it is donned or used in its intended application. Sports T-shirts made from three different fiber types (CO, PES I, PES II) were studied in a pre- and post-purchase scenario by exposing 20 female participants to a hand, a donning (pre-purchase) and running evaluation (post-purchase) in 22 °C and 50% relative humidity (RH). Objective measurements such as skin temperatures, heart rate, body sweat loss, and sweat absorption of the garments were recorded. Subjective data was collected during the fabric hand and the donning evaluation as well as within the running protocol after 5 min, 20 min, and 5 min of cool down. Perceptual responses to 12 hand-/skin-feel descriptors (e.g., rough, smooth) were rated on a scale from 0 (not at all) to 10 (completely) and a feeling of discomfort was given. No significant differences between a hand and a donning evaluation were found in the rating of the sensations. The hand evaluation provided sufficient information for a comfort response to garment wear. The pre- and post-purchase comparison found a significantly lower perception of the feeling of roughness whilst running with the CO shirt and smoothness during running in PES II. The stickiness and comfort perception increased significantly in the post-purchase wear trial. Hence, moisture on the skin provoked through running influences comfort characteristics as well as the perception on haptic cues in t-shirts. Especially surface related haptic characteristics e.g., roughness and smoothness, are reduced.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".