Texture recognition based on multi-sensory integration of proprioceptive and tactile signals
Bibliographic record
Abstract
The sense of touch plays a fundamental role in enabling us to interact with our surrounding environment. Indeed, the presence of tactile feedback in prostheses greatly assists amputees in doing daily tasks. In this line, the present study proposes an integration of artificial tactile and proprioception receptors for texture discrimination under varying scanning speeds. Here, we fabricated a soft biomimetic fingertip including an 8 × 8 array tactile sensor and a piezoelectric sensor to mimic Merkel, Meissner, and Pacinian mechanoreceptors in glabrous skin, respectively. A hydro-elastomer sensor was fabricated as an artificial proprioception sensor (muscle spindles) to assess the instantaneous speed of the biomimetic fingertip. In this study, we investigated the concept of the complex receptive field of RA-I and SA-I afferents for naturalistic textures. Next, to evaluate the synergy between the mechanoreceptors and muscle spindle afferents, ten naturalistic textures were manipulated by a soft biomimetic fingertip at six different speeds. The sensors' outputs were converted into neuromorphic spike trains to mimic the firing pattern of biological mechanoreceptors. These spike responses are then analyzed using machine learning classifiers and neural coding paradigms to explore the multi-sensory integration in real experiments. This synergy between muscle spindle and mechanoreceptors in the proposed neuromorphic system represents a generalized texture discrimination scheme and interestingly irrespective of the scanning speed.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".