Head and Trunk Segment Moments of Inertia Estimation Using Angular Momentum Technique: Validity and Sensitivity Analysis
Bibliographic record
Abstract
Classical models to estimate the head and trunk (HT) moments of inertia (I) are limited to populations from which the anthropometric measures were obtained. The purposes of this study were to determine if the angular momentum technique can be used to estimate subject-specific HT's I values and test its validity and sensitivity. Twenty-three adults who participated in this study were divided into three morphological groups according to their body mass index (BMI). Using the proposed technique, the HT's I values were estimated for the whole sample and compared to three well-known methods to test its validity. The sensitivity of the proposed method was verified while applied to individuals with different BMI (i.e., lean, normal, and obese). The angular momentum technique gave I values within the range of those of the three methods for the entire sample. Statistical differences were identified between the lean and obese groups in relative radii of gyration for the anteroposterior and mediolateral axes ( P<0.05). Since the proposed technique makes no assumption on the mass distribution and segments' geometry, it appeared to be more sensitive to body morphology changes in estimating the HT's I values in lean and obese subjects compared to the classical methods.
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 | 0.018 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".