Adapting Manifold-Based Analyses to Describe How Personal and Task Constraints Impact Lower Extremity Movement Variability During Exercise
Bibliographic record
Abstract
Despite the recent emphasis in the motor control literature quantifying the structure of movement variability, there is a paucity of research applying these concepts in exercise contexts. Measuring the structure of variability surrounding dynamic valgus motion, and how it is impacted by personal and task constraints, can be achieved by adapting Uncontrolled Manifold (UCM) analyses. This thesis aimed to build a foundation for a complementary approach to evaluating the execution of exercise tasks that considers both average movements and movement variability by addressing four main gaps in the literature. First, it is unclear which methods of computing dynamic valgus motion are most related to external knee abduction and external rotation moments. Second, it is unclear how many repetitions are required to monitor the variability surrounding dynamic valgus motion using adapted UCM analyses. Third, although ankle dorsiflexion range of motion (ROM) restrictions can influence the average dynamic valgus motion, it is unclear whether it also impacts the structure of movement variability. Fourth, research has demonstrated that average dynamic valgus motion is more strongly correlated between single-leg exercises. But, no studies have examined these relationships for the structure of movement variability. In Chapter 3, the data demonstrated that computing dynamic valgus motion as the orthogonal distance of the knee joint center from the hip-foot plane (OD) was a relatively better kinematic proxy of external knee abduction and external rotation moments in comparison to Cardan-Euler angles. In Chapter 4, UCM methods were adapted for use with OD. In Chapter 5, the data showed that participants should perform at least five repetitions to obtain saturated variability data for between-participant analyses and at least eight repetitions for within-participant analyses. Chapter 6 demonstrated that individuals’ ankle dorsiflexion ROM had little influence on the structure of movement variability. Chapter 7 found that the task constraints imposed during exercise strongly influence the structure of movement variability. In summary, data from this thesis provide a platform for future research and practice to expand upon a complementary approach to evaluating the execution of exercise tasks by leveraging movement variability data.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".