Existing and Future Guidance on Tactile and Haptic Interactions
Bibliographic record
Abstract
Tactile and haptic interaction is becoming increasingly important in both assistive technologies and special purpose computing environments. ISO 9241 Ergonomics of Human-System Interaction is intended to deal with all modalities of human-computer interactions. While some individual parts do include some general guidance that can apply to tactile and haptic interactions, there are no existing parts providing detailed guidance relating to the particulars of tactile and haptic interactions. This lack of standards leads to serious ergonomic difficulties for users of multiple, incompatible, or conflicting tactile/haptic devices/applications. However, considerable research exists that can be used as the basis for a set of tactile and haptic interaction guidelines. This paper provides an historical perspective of research and standardization efforts and moves towards providing an understanding of the goals of the emerging set of tactile and haptic interaction standards. It discusses how standards are developed and focuses on newly initiated efforts to create a set of parts within ISO 9241 to deal with tactile and haptic interactions. These parts will include Part 900 Introduction, Part 910 Framework, Part 920 Guidelines, Part 930 Multimodal combinations, Part 940 Evaluation methods, and Part 971 Interfaces to publicly available devices. The paper also explains how the reader can get involved in these standardization activities.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.027 | 0.012 |
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".