Does kinematic coupling in the lower extremities and trunk during a chair transfer task change between younger and older adulthood?
Bibliographic record
Abstract
Past work examining relationships across biomechanical domains during gait has observed that age-related effects may be dependent on the joint of interest. This work has not been extended to other functional tasks, nor to relationships within the kinematic domain. Research question: How does kinematic coupling between the lower extremity and trunk segments during a functional task (sit-stand-sit) differ between younger and older adults? Younger ( n =21) and older ( n =28) adults performed a sit-stand-sit task while whole-body motion was tracked. Sagittal foot, shank, thigh, and trunk segment angles were calculated and cross-correlated between pairings of adjacent segments (foot-shank, shank-thigh, and thigh-trunk). Maximum cross-correlation coefficients and their temporal lags quantified the strength and timing of coupling, respectively, and were compared between age groups. Where significant differences were identified, foot-shank and thigh-trunk cross-correlation coefficients were generally stronger, and temporal lags were generally shorter, for older and younger adults, respectively. The thigh-trunk pairing exhibited the opposite trend during the sit-down phase, with stronger cross-correlation coefficients observed for older adults. Significance: These joint-dependent trends may be a marker for the use of compensatory strategies during sit-stand-sit, which may aid in identifying older adults who could benefit from interventions to prevent functional declines in the ability to perform chair transfer tasks. • Kinematic coupling during sit-stand-sit was compared between young and older adults • Foot-shank movement was more closely coupled in older adults • Thigh-trunk movement was more closely coupled in younger adults • Joint-dependent trends may be evident for age-related changes to kinematic coupling • Results may reflect task-specific compensatory strategies during sit-stand-sit
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 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".