Effect of Strength and Speed of Torque Development on Balance Recovery With the Ankle Strategy
Bibliographic record
Abstract
In the event of an unexpected disturbance to balance, the ability to recover a stable upright stance should depend not only on the magnitude of torque that can be generated by contraction of muscles spanning the lower extremity joints but also on how quickly these torques can be developed. In the present study, we used a combination of experimental and mathematical models of balance recovery by sway (feet in place responses) to test this hypothesis. Twenty-three young subjects participated in experiments in which they were supported in an inclined standing position by a horizontal tether and instructed to recover balance by contracting only their ankle muscles. The maximum lean angle where they could recover balance without release of the tether (static recovery limit) averaged 14.9 +/- 1.4 degrees (mean +/- SD). The maximum initial lean angle where they could recover balance after the tether was unexpectedly released and the ankles were initially relaxed (dynamic recovery limit) averaged 5.9 +/- 1.1 degrees, or 60 +/- 11% smaller than the static recovery limit. Peak ankle torque did not differ significantly between the two conditions (and averaged 116 +/- 32 Nm), indicating the strong effect on recovery ability of latencies in the onset and subsequent rates of torque generation (which averaged 99 +/- 13 ms and 372 +/- 267 N. m/s, respectively). Additional experiments indicated that dynamic recovery limits increased 11 +/- 14% with increases in the baseline ankle torques prior to release (from an average value of 31 +/- 18 to 54 +/- 24 N. m). These trends are in agreement with predictions from a computer simulation based on an inverted pendulum model, which illustrate the specific combinations of baseline ankle torque, rate of torque generation, and peak ankle torque that are required to attain target recovery limits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".