Intelligent wearable system design for personalized knee motion and swelling monitoring in osteoarthritis care
Bibliographic record
Abstract
Daily knee monitoring is critical for osteoarthritis management, aiding in both prevention and rehabilitation. Current wearable solutions for daily use typically capture knee-bending angles as a single feature but lack evidence for comprehensive knee-state recognition. Here we introduce SyncKnee, a knee-monitoring system that tracks both joint angles and swelling patterns, providing detailed knee-state monitoring for daily use. SyncKnee consists of three components: a stretch sensor pad, a multi-modal machine-learning model, and personalized information support. The sensor, made from poly(SBS) fiber and eutectic gallium-indium alloy, tracks skin deformation from bending and swelling. Robotic-arm-driven tests confirm sensor accuracy in responding to bending and swelling. In the user study with 15 participants performing five distinct knee maneuvers, our system with a random forest model achieves 98.48% accuracy in recognizing knee behaviors. SyncKnee offers a comprehensive approach to knee monitoring with promising applications for daily osteoarthritis care.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".