VerTouch: A Versatile Training System for Hand Function by Exploring Tactile Composite Perception
Bibliographic record
Abstract
Hand function impairment induced by factors like stroke exerts a serious impact on human life. Hand function training systems, as key technologies for enhancing hand operation ability, have attracted extensive attention. However, existing systems commonly provide only a single-dimensional function, but overlook the composite characteristics of human tactile perception, resulting in poor practicality. To this end, we present VerTouch, a versatile training system for hand function by exploring tactile perception signatures. Specifically, we first investigate the core algorithms utilized in system implementation, including: kinesthetic function quantitative assessment, low-cost haptic signal reconstruction, and haptic perception test threshold generation. Subsequently, the operational realization of VerTouch is described. The system’s hardware framework encompasses a kinesthetic force-feedback interaction module and a haptic recognition module, augmented by a visual software interface. Finally, the key technologies in the VerTouch system are extensive tested, and the experimental results demonstrate that this system has the capacity to satisfy the requirements of compound hand function training.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".