Machine learning analysis of kinematic movement features during functional tasks to discriminate chronic neck pain patients from asymptomatic controls
Bibliographic record
Abstract
This study evaluated the discriminative potential of a machine learning model using movement features during functional tasks to distinguish between patients with non-traumatic chronic neck pain and asymptomatic controls. The study included patients with chronic mechanical neck pain and asymptomatic controls. Inertial sensors analyzed kinematics during two tasks: elevated weight transfer task and water drinking. Movement was characterized using fifteen features, incorporated into machine learning models to assess how movement patterns relate to patient condition. Features included range of motion, peak velocity, smoothness, spatiotemporal inter-plane coordination, energy distribution by frequencies, and movement heterogeneity. Fifty-three patients with neck pain (36.27 ± 14.3 years; 14 men and 39 women) and 53 asymptomatic participants (35.43 ± 14.65 years; 32 men and 21 women) completed the study. Permutation tests evaluated the discriminative potential of neck movement features between groups. The elevated weight transfer task showed significant discriminative power (P = .0337 ± .0239; Accuracy = 0.618 ± 0.02), while the water drinking task did not (P = .215 ± .202). Movement heterogeneity was the most important discriminative feature, with chronic neck pain patients showing higher movement intensity fluctuations over time. Although the elevated weight transfer task showed statistically significant discriminative potential between asymptomatic individuals and those with chronic neck pain, the models correctly classified participants only 61.8% of the time. This result questions the potential of kinematic analysis to identify patients with chronic neck pain. Future research should investigate these models during more challenging tasks in samples of patients with higher neck pain intensity or disability levels.
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 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 | 0.002 | 0.007 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".