Design and Evaluation of an Instrumented Wobble Board for Assessing and Training Dynamic Seated Balance
Bibliographic record
Abstract
Methods that effectively assess and train dynamic seated balance are critical for enhancing functional independence and reducing risk of secondary health complications in the elderly and individuals with neuromuscular impairments. The objective of this research was to devise and validate a portable tool for assessing and training dynamic seated balance. An instrumented wobble board was designed and constructed that (1) elicits multidirectional perturbations in seated individuals, (2) quantifies seated balance proficiency, and (3) provides real-time, kinematics-based vibrotactile feedback. After performing a technical validation study to compare kinematic wobble board measurements against a gold-standard motion capture system, 15 nondisabled participants performed a dynamic sitting task using the wobble board. Our results demonstrate that the tilt angle measurements were highly accurate throughout the range of wobble board dynamics. Furthermore, the posturographic analyses for the dynamic sitting task revealed that the wobble board can effectively discriminate between the different conditions of perturbed balance, demonstrating its potential to serve as a clinical tool for the assessment and training of seated balance. Vibrotactile feedback decreased the variance of wobble board tilt, demonstrating its potential for use as a balance training tool. Unlike similar instrumented tools, the wobble board is portable, requires no laboratory equipment, and can be adjusted to meet the user's balance abilities. While future work is warranted, obtained findings will aid in effective translation of assessment and training techniques to a clinical setting, which has the potential to enhance the diagnosis and prognosis for individuals with seated balance impairments.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".