Audio-visual biofeedback system for postural control
Bibliographic record
Abstract
This study presents an application of biofeedback in balance training, in particular an audio-visual balance biofeedback system for dynamic balance. Motivated by the need to provide portable, cost-effective and accessible training devices, the system implements an accelerometer to quantify the balance board movements during a balancing task and use them to provide a real-time, synchronous audio-visual biofeedback. The visual feedback displays the offset and the overall performance of the balance board. The audio feedback is based on sound localization cues that indicate direction of the balance board movements using stereo sound. Initial results indicate significant improvements in postural stability when audio-visual biofeedback is provided. The study found significant improvements in maintenance and recovery of dynamic balance manifested through decreased variability of balance board dynamics in all directions as well as in lateral and front-to-back directions during balancing tasks when audio-visual biofeedback was used. Future applications will consider embeding the audio-video biofeedback modalities into rehabiliation game training.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".