Aplicação de um modelo musculoesquelético para análise biomecânica da articulação do joelho: efeito agudo da fadiga em atletas de handebol feminino
Bibliographic record
Abstract
This current thesis presents, in a critical perspective, a selection of peer review and/or polished research articles in international journals of biomechanics, motor control and bioengineering fields.In general, the articles present the possible effect of fatigue on posture control and lower limb biomechanics in females handball athletes.Moreover, this text also presents tools for fatigue induction and for biomechanical analysis using the computational modeling and simulation method in OpenSim.The articles were shaped by the academic necessity, that during the doctoral process, were perceived and tried to be understood and remedied.A total of five empirical articles presents (i) a specific method to induce fatigue in handball athletes; (ii) the effect of fatigue on lower limb kinematics; (iii) a musculoskeletal model to knee joint analysis; (iv) a toolbox to optimize modeling and simulation analysis; and lastly (v) the impact of the fatigue in the knee biomechanics: a modelling approach.The text provides a critical reflection on the general contribution to the current body of scientific knowledge.Finally, the limitations of each study are discussed in each article, allowing a direction for future research.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".