Implementing an accelerometer-based pelvis segment for low back kinetic analyses during dynamic movement tasks
Bibliographic record
Abstract
An accelerometer-based pelvis has been employed to study segment and joint kinematics during scenarios involving close human-object interface and/or line-of-sight obstructions. However, its accuracy for examining low back kinetic outcomes is unknown. This study compared reaction moments and contact forces of the L5S1 joint calculated with an accelerometer-based and optically tracked pelvis segment. An equation to correct the global pelvis position as a function of thigh angle was developed. One participant performed four dynamic tasks: forward bend, squat, sit-to-stand-to-sit, and forward lunge. A standard bottom-up inverse dynamics approach was used and the root mean square error (RMSE) and coefficient of determination (R2) were calculated to examine kinetic differences between the optical and accelerometer approaches. The RMSE observed for L5S1 reaction flexion-extension moments ranged from 1.32 Nm to 2.20 Nm (R2 ≥ 0.98). The RMSE for net shear and compression reaction forces ranged from 2.13 to 10.45 N and 0.63 – 4.96 N, respectively. Similarly, the RMSE for L5S1 joint contact shear and compression ranged from 13.45 N to 19.51 N (R2 ≥ 0.85) and 31.18 N – 55.97 N (R2 ≥ 0.97), respectively. In conclusion, the accelerometer-based pelvis together with the linear function to correct the global pelvis position is a feasible approach for computing low back kinetics with a single equivalent muscle model. The observed error in joint contact forces represents less than 5 % of the NIOSH recommended action limits and is unlikely to alter the interpretation of low back injury risk.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".