A structural fingertip model for simulating of the biomechanics of tactile sensation
Bibliographic record
Abstract
Tactile performance of human fingertips is associated with activity of the nerve endings and sensitivity of the soft tissue within the fingertip to the static and dynamic skin indentation. The nerve endings in the fingertips sense the stress/strain states developed within the soft tissue, which are affected by the material properties of the tissues. The vibrotactile sensation and tactile performance are thus believed to be strongly influenced by the nonlinear and time-dependent properties of the soft tissues. The purpose of the present research is to simulate the biomechanics of tactile sensation. A two-dimensional model, which incorporates the essential anatomical structures of a finger (i.e. skin, subcutaneous tissue, bone, and nail), has been used for the analysis. The skin tissue is assumed to be hyperelastic and viscoelastic. The subcutaneous tissue is considered to be a nonlinear, biphasic material composed of a hyperelastic solid and an inviscid fluid phase. The nail and bone are considered to be linearly elastic. The advantages of the proposed fingertip model over the previous "waterbed" and "continuum" fingertip models include its ability to predict the deflection profile of the fingertip surface, the stress and strain distributions within the soft tissue, and most importantly, the dynamic response of the fingertip to mechanical stimuli. The proposed model is applied to simulate the mechanical responses of a fingertip under a line load, and in one-point (1PT) and two-point (2PT) tactile discrimination tests. The model's predictions of the deflection profiles of a fingertip surface under a line load agree well with the reported experimental data. Assuming that the mechanoreceptors in the dermis sense the stimuli associated with normal strains (the vertical and horizontal strains) and strain energy density, our numerical results suggest that the threshold of 2PT discrimination may lie between 2.0 and 3.0 mm, which is consistent with the published experimental data. The present study represents an effort to develop a structural model of the fingertip that incorporates its anatomical structure, and the nonlinear and time-dependent properties of the soft tissues.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".