The Use of Vibrotactile Feedback to Restore Texture Recognition Capabilities, and the Effect of Subject Training
Bibliographic record
Abstract
This paper presents a vibrotactile haptic feedback system for use under dynamic conditions, verifies its functionality, and shows how results may be affected by the amount of training that subjects receive. We hope that by using vibrotactile feedback to distinguish between different textures, upper-limb amputees may be able to partially regain the sense of touch. During a previous experiment (Motamedi et al., 2015) we noticed a correlation between how familiar the subjects were with haptic systems, and how well they were able to use the haptic system to accurately identify textures. This observation lead us to conduct a second experiment, the results of which are the main focus of this paper. We began with a group of subjects who were completely unfamiliar with haptic systems, and tracked the improvements in their accuracy over a period of four weeks. Although the subjects showed a 16% improvement in their ability to recognize textures, going from a 64% success rate after the first week to 80% after the fourth, perfect accuracy was not attained. A subsequent experiment, however, shows that this result should not diminish our perception of the haptic system's effectiveness. When we asked the same subjects to identify the textures using only their fingertips, we found that even humans cannot distinguish between near-identical textures with complete accuracy. The subjects' overall success rate when using their own hands was 91%, demonstrating that the proposed haptic system is not far from achieving the same texture recognition capabilities as the human sense of touch.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".