Characterizing trunk muscle activations during simulated low-speed rear impact collisions
Bibliographic record
Abstract
Objective: The purpose of this study was to evaluate the activation profiles of muscles surrounding the lumbar spine during unanticipated and braced simulated rear-end collisions.Methods: Twenty-two low-speed sled tests were performed on 11 human volunteers (△V = 4 km/h). Each volunteer was exposed to one unanticipated impact and one braced impact. Accelerometers were mounted on the test sled and participants’ low back. Six bilateral channels of surface electromyography (EMG) were collected from the trunk during impact trials. Peak lumbar accelerations, peak muscle activation delay, muscle onset time, and peak EMG magnitudes, normalized to maximum voluntary contractions (MVCs), were examined across test conditions.Results: Though not statistically significant, bracing for impact tended to reduce peak lumbar acceleration in the initial rearward impact phase of the occupant’s motion by approximately 15%. The only trunk muscles with peak activations exceeding 10% MVC during the unanticipated impact were the thoracic erector spinae. Time of peak muscle activation was slightly longer for the unanticipated condition (unanticipated = 296 ms; braced = 241 ms).Conclusions: Results from this investigation demonstrate that during an unanticipated low-speed rear-end collision, the peak activation of muscles in the lumbar spine are low in magnitude. As such, muscle activation likely has minimal contribution to the internal joint loads that are experienced in the lumbar intervertebral joints during low-speed rear impact collisions. These findings justify the use of simplified joint models in estimating the joint loads in the lumbar spine during low-speed rear impact collisions and support the application of cadaveric and anthropomorphic test device (ATD) testing in understanding the resultant joint loads in the lumbar spine associated with rear-end collisions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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 teacher head, 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".