Multichannel Vibrotactile Glove: Validation of a New Device Designed to Sense Vibrations
Bibliographic record
Abstract
There is a growing interest in using the tactile modality as a compensation or sensory augmentation tool in various fields. The Multichannel Vibrotactile Glove was designed to meet the needs of these diverse disciplines and overcome the limitations of current sound-to-touch technologies. Using 12 independent haptic exciters on each finger's back and on the palm, the device can convey acoustic information to cutaneous vibrotactile receptors with precise control of the location, frequency, timing, and intensity. A staircase method was used to model vibration detection thresholds at six frequencies (100, 200, 250, 500, 800, 1000 Hertz) for each actuator position (All, Thumb, Index, Major, Middle, Pinky, Palm) and both hands (Right, Left). No between hand difference was observed and all finger actuators provided consistent thresholds, except for the Palm which exhibited higher thresholds. Spatial summation effects were observed when all actuators were activated simultaneously. Detection thresholds significantly increased at 100 Hertz and above 500 Hertz. These findings confirm that the system provides uniform stimulation across hands and actuators. Overall, the Multichannel Vibrotactile Glove provides the freedom to send various acoustic features to individual actuators, providing a versatile tool for research and a potential technology to substitute, compensate, or extend sensory perception.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".