Examination of Tactor Configurations for the Design of Vibrotactile Feedback Systems for Use in Lower-Limb Prostheses
Bibliographic record
Abstract
Vibrotactile feedback may be able to compensate for the loss of sensory input in lower-limb prosthesis users. Designing an effective vibrotactile feedback system would require that users could perceive and correctly respond to vibrotactile stimuli applied by the tactors. Our study explored three key tactor configuration variables (i.e. vibratory intensity, prosthetic pressure, spacing between adjacent tactors) through two experiments. The vibration propagation experiment investigated the effects of tactor configurations on vibratory amplitude at the prosthesis-limb interface. Results revealed a positive relationship between vibratory amplitude and intensity, and a negative relationship between vibratory amplitude and prosthetic pressure. The vibrotactile perception experiment investigated the effects of tactor configurations on user response accuracy, and found that greater spacing between tactors, and higher prosthetic pressure resulted in more accurate responses from the subjects. These findings inform the design of a vibrotactile feedback system for use in lower-limb prostheses: 1) the tactors may be best placed in areas of slightly elevated pressure at the prosthesis-limb interface; 2) a higher vibratory intensity level should improve performance for vibrotactile feedback systems; and 3) more spacing between adjacent tactors improves user response accuracy.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".