Movement Quality: A Novel Biomarker Based on Principles of Neuroscience
Bibliographic record
Abstract
A major problem in neurorehabilitation is the lack of objective outcomes to measure movement quality. Movement quality features, such as coordination and stability, are essential for everyday motor actions. These features allow reacting to continuously changing environment or to resist external perturbations. Neurological disorders affect movement quality, leading to functionally impaired movements. Recent findings suggest that the central nervous system organizes motor elements (eg, muscles, joints, fingers) into task-specific ensembles to stabilize motor tasks performance. A method to quantify this feature has been previously developed based on the uncontrolled manifold (UCM) hypothesis. UCM quantifies movement quality in a spatial-temporal domain using intertrial analysis of covariation between motor elements. In this point-of-view article, we first describe major obstacles (eg, the need for group analysis) that interfere with UCM application in clinical settings. Then, we propose a process of quantifying movement quality for a single individual with a novel use of bootstrapping simulations and UCM analysis. Finally, we reanalyze previously published data from individuals with neurological disorders performing a wide range of motor tasks, that is, multi-digit pressing and postural balance tasks. Our method allows one to assess motor quality impairments in a single individual and to detect clinically important motor behavior changes. Our solution may be incorporated into a clinical setting to assess sensorimotor impairments, evaluate the effects of specific neurological treatments, or track movement quality recovery over time. We also recommended the proposed solution to be used jointly with a typical statistical analysis of UCM parameters in cohort studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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".