Comparing A Novel Smartphone Application To The Kinect V2 For Assessing ACL Injury Risk
Bibliographic record
Abstract
Screening for athletes at high risk for anterior cruciate ligament (ACL) tear who may optimally benefit from risk reduction programs remains a challenge. Initial coronal (IC), peak coronal (PC), and peak sagittal (PS) angles of a drop vertical jump (DVJ) measured by the Kinect v2 device (Microsoft) have been associated with ACL tear risk in collegiate varsity athletes. Development of a similar modality utilizing a smartphone application would improve simplicity, cost, and accessibility, allowing for widespread screening. PURPOSE: To compare the DVJ tracking performance of a novel smartphone application to the previously validated Kinect v2 device. METHODS: Two hundred fifty-two collegiate varsity athletes performed three DVJs that were recorded simultaneously with a novel smartphone application developed using the Google Mediapipe framework version 0.7 (Pixel 6; Google) and the Kinect v2. Agreement on IC, PC, and PS angles between the two devices was compared using intraclass correlation coefficient (ICC) tests (two-way mixed-effects model, consistency, single measures). High-risk athletes were identified based on published angle cutoffs for Kinect v2 measurements. Receiver operating characteristic (ROC) analysis was used to assess the accuracy of the smartphone application in identifying the same high-risk athletes. RESULTS: ICC values for IC, PC, and PS angles were 0.696 (0.627 - 0.755, P < 0.01), 0.664 (0.589 - 0.728, P < 0.01), and 0.756 (0.697 - 0.804, P < 0.01), respectively, representing good agreement for IC and PC angles and excellent agreement for PS angles. Ninety-six (38%), 23 (9%), and 90 (36%) high-risk athletes were identified based on IC, PC, and PS angle cutoffs for the Kinect v2. ROC analysis demonstrated an area under the curve of 0.89 (0.85 - 0.93), 0.85 (0.77 - 0.93), and 0.89 (0.85 - 0.93) for identifying these high-risk athletes using the smartphone application, indicating excellent accuracy for all parameters. Smartphone application-derived IC, PC, and PS angle cutoffs of 0.74°, 2.20°, and 73.50° identified 83%, 78%, and 84% of high-risk athletes, respectively. CONCLUSIONS: The novel smartphone application identifies athletes marked as high-risk for ACL tear from Kinect v2 cutoffs with excellent accuracy and shows potential for use in widespread screening. This work was supported by MEDTEQ+, Emovi Inc., and Semperform Inc.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".