Role of the Unperturbed Limb and Arms in the Reactive Recovery Response to an Unexpected Slip During Locomotion
Bibliographic record
Abstract
Understanding reactive recovery responses to slipping is fundamental in falls research and prevention. The primary purpose of this study was to investigate the role of the unperturbed limb and arms in the reactive recovery response to an unexpected slip. Ten healthy, young adults participated in this experiment in which an unexpected slip was induced by a set of steel free-wheeling rollers. Surface electromyography (EMG) data were collected from the unperturbed limb (i.e., the swing limb) rectus femoris, biceps femoris, tibialis anterior, and the medial head of gastrocnemius, and bilateral gluteus medius, erector spinae, and deltoids. Kinematic data were also collected by an optical imaging system to monitor limb trajectories. The first slip response was significantly different from the subsequent recovery responses to the unexpected slips, with an identifiable reactive recovery response and no proactive changes in EMG patterns. The muscles of the unperturbed limb, upper body, and arms were recruited at the same latency as those previously found for the perturbed limb. The arm elevation strategies assisted in shifting the center of mass forward after it was posteriorly displaced with the slip, while the unperturbed limb musculature demonstrated an extensor strategy supporting the observed lowering of the limb to briefly touch the ground to widen the base of support and to increase stability. Evidently a dynamic multilimb coordinated strategy is employed by the CNS to control and coordinate the upper and lower limbs in reactive recovery responses to unexpected slips during locomotion.
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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".