Exploring the Tactor Configurations of Vibrotactile Feedback Systems for Use in Lower-Limb Prostheses1
Bibliographic record
Abstract
Vibrotactile feedback may be able to compensate for the loss of sensory input in lower-limb prosthesis users to improve the mobility function. Designing an effective vibrotactile feedback system requires that users are able to perceive and respond to vibrotactile stimuli correctly and in a timely manner. Our study explored four key tactor configuration variables (i.e., tactors’ prosthetic layer, vibration intensity, prosthetic pressure, and spacing between adjacent tactors) through two experiments. The vibration propagation experiment investigated the effects of tactor configurations on vibration amplitude at the prosthesis–limb interface. Results revealed a positive relationship between vibration amplitude and intensity and a weak relationship between vibration amplitude and prosthetic pressure. Highest vibration amplitudes were observed when the tactor was located on the inner socket layer. The second experiment involving a sample of ten able-bodied and three amputee subjects investigated the effects of tactor configurations on user perception measured by response time, accuracy identifying tactors’ stimulation patterns, and spatial error in locating the tactors. Results showed that placing the tactors on the inner socket layer, greater spacing between adjacent tactors, and higher vibration intensity resulted in better user perception. The above findings can be directly applied to the design of vibrotactile feedback systems to increase the user response accuracy and decrease the response time required for dynamic tasks such as gait. They can also help to inform future clinical trials informing the optimization of tactor configuration variables.
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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 | 0.002 |
| 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) | 0.002 | 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".