Comparison of Gait Pattern in Athletes with ACL Deficiency and Healthy Individual using an Accelerometer
Bibliographic record
Abstract
Background: In athletes with anterior cruciate ligament (ACL) deficiencies could assess functional capabilities with different instruments such as use of a camera in vivo situation. However, these methods have suffered from a large number of limitations such as inability to be repeatable and complexity in technique. Objective: The main purpose of this study was to compare gait pattern of the athletes with ACL injury and able-bodied subjects using an accelerometer. Method: A three-dimensional accelerometer was placed over the tibia tuberosity of 20 healthy and 20 individuals with ACL-deficiencies (ACLD). After walking on the treadmill, the principal components of the acceleration data were calculated using MATLAB software. Results: In this study, Principle Component analysis was used for statistical analysis. The results indicated that subjects with ACL deficiency have different gait pattern compared to the control group. The major differences between stride trajectories of the two groups were at the end of mid-swing and the beginning of terminal swing phases in vertical axis. ACL deficient subjects exhibited different gait patterns during mid and terminal stance phases in anterior- posterior axis compared with normal controls. Conclusions: The difference in gait between subjects with ACL deficiency and healthy subjects are depends on variation in the amount of knee flexion and tibia rotation that could be altered to motor recruitment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".