Different Strokes and Different Folks
Bibliographic record
Abstract
Social touch is an essential non-verbal channel whose great interactive potential can be realized by the ability to recognize gestures performed on inviting surfaces. To assess impact on recognition performance of sensor motion, substrate and coverings, we collected gesture data from a low-cost multitouch fabric pressure-location sensor while varying these factors. For six gestures most relevant in a haptic social robot context plus a no-touch control, we conducted two studies, with the sensor (1) stationary, varying substrate and cover (n=10); and (2) attached to a robot under a fur covering, flexing or stationary (n=16). For a stationary sensor, a random forest model achieved 90.0% recognition accuracy (chance 14.2%) when trained on all data, but as high as 94.6% (mean 89.1%) when trained on the same individual. A curved, flexing surface achieved 79.4% overall but averaged 85.7% when trained and tested on the same individual. These results suggest that under realistic conditions, recognition with this type of flexible sensor is sufficient for many applications of interactive social touch. We further found evidence that users exhibit an idiosyncratic `touch signature', with potential to identify the toucher. Both findings enable varied contexts of affective or functional touch communication, from physically interactive robots to any touch-sensitive object.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".