Pilot Data on the Performance of Vibrotactile Actuators for Use with Assistive Devices
Bibliographic record
Abstract
As human-machine interfacing becomes increasingly common, the importance of biofeedback optimization escalates. Many machine-user interfaces currently require the use of biofeedback; for instance, fall prevention and posture improvement devices often make use of vibrotactile biofeedback to communicate to the user the necessary body motions to regain balance or maintain a proper posture, thereby enhancing or replacing the body's natural position biofeedback system and closing the biofeedback loop. Vibrotactile biofeedback could also be combined with an exoskeleton or a powered orthotic for the aforementioned purposes in users that require machine assistance to vary their body position. Although these devices employ biofeedback, there exists no thorough research on the optimization of vibrotactile biofeedback parameters such as actuator location on the body, actuator type or information coding method. Some studies researched the implications of one or two of these parameters, but no study has considered all 3 parameters. This study aims to optimize biofeedback by minimizing the user's reaction time and discomfort, and by enhancing the ability to correctly identify the tactor activated and its intensity. In order to determine the optimal biofeedback configuration the tactor type, information coding method and actuator location on the user's body were varied and the combination showing the best overall results was selected as the optimal biofeedback configuration.
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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 | 0.002 | 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".