Playing with Feeling: Exploring Vibrotactile Feedback and Aesthetic Experiences for Developing Haptic Wearables for Blind and Low Vision Music Learning
Bibliographic record
Abstract
Musical haptic wearables (MHWs) that convey information through vibrotactile feedback holds the potential to support the music learning of a blind or low vision (BLV) music learner. Yet, it is unclear how these technologies can give functional support to a BLV person. We also investigated material preferences in such technologies to understand the role of non-functional aesthetic experiences in shaping their music learning. We conducted 5 co-design workshops with 10 BLV participants. Participants explored eleven materials common in a music learning environment and engaged in bodystorming with a prototype that communicated six vibrotactile patterns. Through thematic analysis, we found that MHWs with vibrotactile alerts and variations in vibration are suited to communicate instructional information, aid music reading and support technical guidance and practice. We categorized the participants’ material experiences into sensorial, interpretive, and affective levels. Based on our findings, we discuss considerations when designing vibrotactile interactions to support music learning for BLV people and highlight material experiences that should be emphasized to make the music learning experience wholesome for BLV music learners.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".