QUANTIFICATION OF MASS AND CENTER-OF-MASS OF HEALTHY AND AMPUTATED SEGMENTS AS WELL AS FULL-BODY CENTER-OF-MASS OF AMPUTEES
Bibliographic record
Abstract
Quantification of segment-inertial uniqueness can provide a relevant foundation for motion analysis, biomechanical modeling and human motor skill optimization of both normal and amputated athletes. It is known that previous studies focused on quantifying Body Segment Inertial Parameters (BSIP) of non-amputated people in order to establish regression equations for calculating BSIPs. Until now, no anthropometrical study existed on quantifying BSIPs such as mass and center of mass (COM) of both non-amputated segment (NAS) and partially-amputated segment (PAS) of amputees. This study aims to fill the gap. A quantification method derived from Damavandi approach was applied to determine the mass and COM of PAS as well as full-body COM. For validating the reliability of this method, the calculated values were compared to the values measured by balance board test. Further, two anthropometrical approaches (i.e. Zheng and Zatsiorsky) for normal subjects were tested for their validity to estimatfe the mass and COM of NASs of amputees. The results reveal that Damavandi approach can also be used for reliable quantifying of mass and COM of PAS and Zatsiorsky’s approach is more reliable to quantify NAS masses and full-body COM of amputees, therefore, Damavandi approach and Zatsiorsky’s regression model are more suitable for motion analysis, biomechanical modeling and motor skill optimization of amputees.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".