Design and validation of a novel 3D-printed wearable device for monitoring knee joint kinematics
Bibliographic record
Abstract
Gait analysis provides an important tool for the study and clinical evaluation of conditions which affect knee joint biomechanics. Collection of knee joint kinematics in real world environments during locomotor activities of daily living could provide quantitative evidence to help understand functional impairment. Unfortunately, the high cost and necessary technical expertise associated with current commercially available systems for kinematic monitoring serve as an impediment to their adoption outside of specialized research groups. We have developed a low-cost, custom wearable device to address these shortcomings. The 3D printed device is capable of measuring knee flexion/extension (F/E) and adduction/abduction (AD/AB) angles. Here, we present a gold standard validation of the novel device against an optoelectronic motion capture system (MCS). Data were collected during a treadmill walking task from 8 participants on 2 separate occasions. Agreement with the MCS was quantified via root mean squared error (RMSE), coefficients of multiple correlation (CMC), paired dependent t-tests and Bland-Altman analyses. The wearable device had an overall RMSE of 3.0° and 2.7° and a CMC of 0.97 and 0.91 in F/E and AD/AB respectively. Wearable device error showed no significant differences between test occasions, and Bland-Altman analyses showed low bias with narrow limits of agreement. These results demonstrate the capability of the device to accurately and reliably monitor knee F/E and AD/AB angles showing strong potential for field implementation.
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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 | 0.000 |
| 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".