Human tactile perception as a standard for artificial tactile sensing—a review
Bibliographic record
Abstract
In this paper, we examine the most important features of human skin tactile properties with special emphasis on the characteristics which are vital in the design of artificial systems. Contrary to the visual and auditory senses, the touch signal is not a well-defined quantity. As a result, the researchers of this field are still dealing with the basics of collecting the most relevant data. Following this, mimicking the sense of touch by producing artificial tactile skin is a challenging process. Although the sense of touch is widely distributed all over the human body, the tactile perception in the human hand is of great importance in terms of surgical and medical robotics applications. In this study, the role of various mechanoreceptors in the human hand, such as, RA, SA I, SA II, and PC units are discussed in relation to the stimuli like force, position, softness, and surface texture. Taking human hand as a suitable tactile model, the necessary engineering features of an artificial tactile sensor, such as, spatial and temporal resolutions, force sensitivity, and linearity, are being reviewed. In this work, we also report on the current and possible future applications of tactile sensors in various surgical procedures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".