An Accelerometer as an Alternative to a Force Plate for the Step-Up-and-Over Test
Bibliographic record
Abstract
The step-up-and-over test has been used successfully to examine knee function after knee injury. Knee function is quantified using the following variables extracted from force plate data: the maximal force exerted during the lift, the maximal impact force at landing, and the total time to complete the step. For various reasons, including space and cost, it is unlikely that all clinicians will have access to a force plate. The purpose of the study was to determine if the step-up-and-over test could be simplified by using an accelerometer. The step-up-and-over test was performed by 17 healthy young adults while being measured with both a force plate and a 3-axis accelerometer mounted at the low back. Results showed that the accelerometer and force plate measures were strongly correlated for all 3 variables (r = .90-.98, Ps < .001) and that the accelerometer values for the lift and impact indices were 6-7% higher (Ps < .01) and occurred 0.07-0.1 s later than the force plate (Ps < .05). The accelerometer returned values highly correlated to those from a force plate. Compared with a force plate, a wireless, 3-axis accelerometer is a less expensive and more portable system with which to measure the step-up-and-over test.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".