Experimental simulation of cyclic, six degree-of-freedom, gait and sit-to-stand loading waveforms using a six-axis joint motion simulator
Bibliographic record
Abstract
In vitro spinal load simulation is a critical tool for understanding the biomechanics of the spine. However, cyclic loading for these in vitro experiments is commonly limited to one or two degrees-of-freedom (DOF). While easily adopted, these methods do not capture the 6DOF loading associated with spinal movements and activities. The goal of this study was to develop and evaluate a comprehensive in vitro testing method to apply cyclic 6DOF gait and sit-to-stand (S2S) loading profiles to lumbar spinal motion segments. Eight cadaveric lumbar segments were subjected to 1DOF pure moment testing in load control for simulated flexion-extension, lateral bending, and axial rotation, followed by 6DOF gait and S2S simulations for short-duration tests (5 cycles). Gait and S2S tests were compared at quasi-static (0.5Nm/s) and dynamic (5Nm/s) loading rates. 6DOF gait simulations were also simulated over a longer-duration test (10,000 steps), with comparison of the pre- and post-cycle movement response examined. Load control testing with iterative learning control (ILC) was employed to ensure load accuracy during longer-duration gait simulations. Following 10,000 steps, no significant changes in spinal range of motion were observed. Root mean square error remained below simulator load cell resolution, except during the short-duration dynamic tests of gait and sit-to-stand in compression and flexion-extension. During longitudinal gait testing, convergence was reached at 4-10 % of total test length in all actuators. This study highlights the feasibility of simulating real-world loading conditions, such as walking and S2S activities, to better evaluate lumbar spine biomechanics under physiologically-derived loading conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".