FabriCar: Enriching the User Experience of In-Car Media Interactions with Ubiquitous Vehicle Interiors using E-textile Sensors
Bibliographic record
Abstract
This work explores e-textiles in the design space of Human-Vehicle Interaction (HVI) and compares distraction levels between e-textile and screen-based interactions during driving tasks. We developed three prototypes (in the steering wheel, headrest cover, and seat-belt pad) to support tactile interactions (tap, press, and swipe) with car interior elements for non-driving applications (such as media control). Our designs used digital embroidery to achieve aesthetic design qualities and wireless connection. In a deployment study with 16 participants, we collected quantitative and qualitative data through video recording, field observations, and user interviews. The study repeated all scenarios using screen-based interaction for comparison. Our findings present insights into fabric-based sensors including fewer collisions and a 302.7% decrease in eye distraction. These findings suggest new design opportunities, such as retrofitting existing vehicles, designing ideation toolkits for diverse users, devising an e-textile Fitts’ Law for reachability, and expanding vehicle interaction research within the HCI community.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".