Muscle forces and activations in Parkinson’s disease:a model-based approach
Bibliographic record
Abstract
Parkinson’s disease (PD) is a progressive pathological condition caused by a dopamine deficiency. Although gait alterations are well-known in PD patients, changes in neural strategies have recently been explored. The presented study aims to address the advantages of adopting a neuromusculoskeletal modelling approach, in order to detect alterations in PD’s motor control and report differences in knee and ankle muscle forces with respect to the healthy individuals. The adopted electromyography (EMG)-informed computational model was fed by EMG signal coupled with 3D marker trajectories and ground reaction forces. Ten PD subject-specific models were developed and compared with a control group of 13 subjects matched for age and BMI. Results showed significant differences in the neuromuscular control strategy of the PD group both in terms of muscle forces and co-contraction index. The estimated variables can become a measurable outcome in order to assess the effect of physical therapy interventions thus allowing to track the disease progression. Furthermore, this technology might be adopted to plan interventions through exoskeletons, by providing an estimate of the degree of muscle forces required by the specific subject to restore a physiological gait profile.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".