Effects of Noisy Galvanic Vestibular Stimulation During a Bimanual Tracking Robotic Task
Bibliographic record
Abstract
BACKGROUND: Noisy galvanic vestibular stimulation (nGVS) has been shown to improve motor performance in people with and without disabilities. Previous investigations on the use of nGVS to improve upper-limb motor performance have focused on unimanual fine motor movements, nevertheless, bimanual gross movements are also essential for conducting activities of daily living and can be affected as a result of cerebral dysfunction. Consequently, in this study we investigated the effects of nGVS on bimanual gross motor performance. METHODS: Twelve healthy participants completed a visuomotor task in which they performed bimanual upper-limb movements using two robots. During the task, participants tracked a target that oscillated following a sinusoidal amplitude-modulated trajectory. In half of the trials, participants received subthreshold nGVS, in the other half, they received sham stimulation. Primary outcome measure: percent improvement in root mean square error (RMSE) between the target's and cursors' trajectories. Secondary outcome measures: percent improvement in lag between the cursors and target; and percent improvement in RMSE between the cursors' trajectories. A post-test questionnaire was administered to evaluate the experience of participants. RESULTS: = 0.098). Similar results were found for the lag. Questionnaire results indicated that the robotic devices did not limit participants' movements, did not make participants feel unsafe, nor were they difficult to control. Furthermore, participants did not feel unsafe with the nGVS device, nor did they report any discomfort due to nGVS. CONCLUSION: Results suggest that nGVS applied to people without disabilities do not affect bimanual gross motor performance. However, as this was the first study to investigate such effects, stimulation parameters were based on previous unimanual fine motor studies. Future studies should investigate optimal stimulation parameters for improving upper-limb gross motor performance. Overall, participants felt safe using the robotic devices and receiving the noisy electrical stimulation. As such, a similar setup could potentially be employed for subsequent studies investigating the relation between upper-limb performance and nGVS.
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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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.000 |
| 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".