Proposal and Validation of a Knee Measurement System for Patients With Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: Currently most measurements of knee joint function are obtained through observation and patient-reported outcomes. This paper proposes an implementation and validation of a knee monitor to measure quantitative joint data in multiple degrees of freedom. The proposed system is configurable with minimal patient interaction and no frame-alignment calibration procedure is required for measurement after visually placing/replacing sensors on patients. METHODS: A mobile software system was developed using a method of extracting clinical knee angles based on attitude estimations from independent wearable sensors. Validation was performed using a robot phantom and results were compared with a gold standard motion capture system. Two instrumentation placements (lateral and posterior) were examined. RESULTS: A posterior sensor placement was determined to provide the most repeatable results through multiple degrees of freedom and measurement accuracy approached a gold standard motion capture technology with low root-mean-square error (flexion: 3.34°, internal/external rotation: 2.18°, and varus/valgus: 1.44°). CONCLUSION: The proposed system is simple to use and convenient for use in ambulatory or unsupervised environments for joint measurement; however, it was shown that accuracy can be sensitive to sensor placement. SIGNIFICANCE: This system would be beneficial for obtaining quantitative patient data or tracking functional activity in variable environments, providing clinicians with indications of how patients' knees function during activity, potentially permitting more individualized care and recommendations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".