Medicine in novel technology and devices quantitative study and evaluation of ankle joint motor-cognitive dual-task post-stroke using eye-tracking technology
Bibliographic record
Abstract
Dual-task ability is crucial for daily life, but sensory, cognitive, and motor impairments often reduce performance in patients, significantly impacting their quality of life. To evaluate and restore this ability, this study proposes an eye-tracking-based dual-task training system for ankle movement and cognition. The system is designed to capture and analyze real-time ankle and eye movement parameters, integrating these with traditional clinical scales to offer a multidimensional, objective, and quantitative evaluation standard. Reliability and criterion validity analyses involving 20 healthy adults and 30 stroke patients demonstrated that 88.2% of the evaluation parameters exhibited high consistency, with 55.8% showing a moderate correlation with clinical benchmark scales (p<0.05). Notably, the Montreal Cognitive Assessment (MOCA), dual-task cost percentage, and TUG-subtraction task duration were identified as key indicators of dual-task ability, while the Self-Rating Anxiety Scale showed lower sensitivity. Furthermore, foot and ankle motion parameters exhibited a strong correlation with balance and fall risk, effectively serving as predictors of motor function recovery and fall risk in stroke patients. The system provides an innovative, quantitative tool for assessing lower limb dual-task ability, facilitating the identification of dual-task performance differences among stroke patients. It supports the development of evidence-based rehabilitation strategies, with the potential to enhance long-term functional recovery and improve patients' quality of life.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".