Exploring the Role of Haptic Feedback in Enabling Implicit HCI-Based Bookmarking
Bibliographic record
Abstract
We examine how haptic feedback could enable an implicit human-computer interaction, in the context of an audio stream listening use case where a device monitors a user's electrodermal activity for orienting responses to external interruptions. When such a response is detected, our previously developed system automatically places a bookmark in the audio stream for later resumption of listening. Here, we investigate two uses of haptic feedback to support this implicit interaction and mitigate effects of noisy (false-positive) bookmarking: (a) low-attention notification when a bookmark is placed, and (b) focused-attention display of bookmarks during resumptive navigation. Results show that haptic notification of bookmark placement, when paired with visual display of bookmark location, significant improves navigation time. Solely visual or haptic display of bookmarks elicited equivalent navigation time; however, only the inclusion of haptic display significantly increased accuracy. Participants preferred haptic notification over no notification at interruption time, and combined haptic and visual display of bookmarks to support navigation to their interrupted location at resumption time. Our contributions include an approach to handling noisy data in implicit HCI, an implementation of haptic notifications that signal implicit system behavior, and discussion of user mental models that may be active in this context.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".