Experimental Performance Evaluation of Human Balance Control Models
Bibliographic record
Abstract
Two factors commonly differentiate proposed balance control models for quiet human standing: 1) intermittent muscle activation and 2) prediction that overcomes sensorimotor time delays. In this experiment we assessed the viability and performance of intermittent activation and prediction in a balance control loop that included the neuromuscular dynamics of human calf muscles. Muscles were driven by functional electrical stimulation (FES). The performance of the different controllers was compared based on sway patterns and mechanical effort required to balance a human body load on a robotic balance simulator. All evaluated controllers balanced subjects with and without a neural block applied to their common peroneal and tibial nerves, showing that the models can produce stable balance in the absence of natural activation. Intermittent activation required less stimulation energy than continuous control but predisposed the system to increased sway. Relative to intermittent control, continuous control reproduced the sway size of natural standing better. Prediction was not necessary for stable balance control but did improve stability when control was intermittent, suggesting a possible benefit of a predictor for intermittent activation. Further application of intermittent activation and predictive control models may drive prolonged, stable FES-controlled standing that improves quality of life for people with 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".