Evaluation of EMG-Assisted and EMG-Driven control modes in patients with medial compartment knee osteoarthritis
Bibliographic record
Abstract
Knee osteoarthritis (OA) is associated with higher-than-normal knee joint contact forces (KJCFs) during walking which cannot be easily measured. KJCFs can be estimated using neuromusculoskeletal (NMSK) modelling in electromyogram (EMG)-driven and −assisted control modes. Previous research has not examined which control mode is most appropriate for estimating KJCFs in patients with knee OA. This study aimed to evaluate a NMSK modelling framework using both control modes in patients with medial-dominant knee OA. First, EMG-assisted mode was hypothesized to better track ID-computed joint moments. Second, KJCFs estimated using the two control modes were hypothesized to differ. Gait data were measured from 27 patients with medial-dominant tibiofemoral knee OA. An OpenSim model was scaled to patient-specific anthropometrics. Inverse kinematics, inverse dynamics, and muscle analysis were performed. Resulting joint angles, moments, musculotendon kinematics, and muscle activations were input into the Calibrated Electromyography Informed Neuromusculoskeletal Modelling Toolbox. Muscle forces and KJCFs were estimated using EMG-driven and −assisted control modes. EMG-assisted mode better tracked knee flexion moments (RMSE = 2.7 ± 2.1Nm, R 2 = 0.9 ± 0.1) and estimated a higher, albeit non-significant, second peak medial compartment KJCF (2.3 ± 1.1BW) compared to EMG-driven mode (RMSE = 12.6 ± 3.9Nm, R 2 = 0.6 ± 0.2, KJCF = 2.1 ± 0.9BW). EMG-assisted control mode may therefore be more appropriate for evaluating KJCFs in patients with knee OA.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".