Modelling the dynamic response of the human spine to shock and vibration using a recurrent neural network
Bibliographic record
Abstract
The ability to model the spine's response to mechanical shock and vibration is an important step in assessing the health hazards of repeated impacts to vehicle passengers. Current linear models, such as the Dynamic Response Index (DRI) and the British Standard 6841 filter (BS 6841), perform poorly when the input consists of large–magnitude shocks typical of those experienced by personnel in military vehicles. In this study, a recurrent neural network (RNN) was developed which models the spinal acceleration response of the seated passenger at the LA vertebra to vertical accelerations applied at the seat. ARNN is a universal nonlinear approximator that can, in theory, model any system with memory if trained with a representative set of measured input–output data. The seat–spine system was modelled as a network with four inputs and one output. The back propagation algorithm was used to train the network by adjusting network parameters to minimise the square of the prediction error. The inputs to the network were delayed values of the inputs and outputs. The trained network significantly outperformed the two linear models examined for predicting the z–axis acceleration at the L4 vertebra.
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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 | 0.001 | 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".