Investigating Cervical Muscle Response and Head Kinematics During Right, Left, Frontal and Rear-Seated Perturbations
Bibliographic record
Abstract
OBJECTIVE: Whiplash research has largely focused on rear collisions because they account for the majority of whiplash injuries. The purpose of this study was to evaluate the effects of 4 perturbation directions (anterior, posterior, right, and left) on muscle activity and head kinematics to provide insight into the whiplash mechanism of injury. METHODS: The effects of 4 perturbation directions induced by a parallel robotic platform, with peak acceleration of 8.50 m/s2, were analyzed on 10 subjects. Surface electromyography (EMG) measures were collected from the sternocleidomastoid (SCM), trapezius, and splenius capitus muscles. Kinematics of the head, thorax, and head relative to thorax were also measured. RESULTS: We observed stereotypic responses for kinematics and SCM EMG for the various perturbation directions; the trapezius and splenius capitus muscles showed amplitudes that were less than 5 percent maximum voluntary contraction (MVC). Rear perturbations elicited the smallest onset latencies for the SCM (30 ms) and kinematic variables and greatest linear head center of mass (COM) accelerations. Frontal perturbations resulted in an average SCM onset latency of 143 ms and demonstrated the greatest magnitude of head translations and rotations relative to the thorax. Left and right perturbations demonstrated similar kinematics and SCM onset latencies (55 and 65 ms, respectively). CONCLUSIONS: Compared to frontal, left, and right directions, rear perturbations showed smaller SCM onset latencies, greater SCM amplitudes, and larger head accelerations, relating to a greater potential for injury. We suggest that the greater contact area and stiffness of the seatback, in the posterior direction, compared to restrictions in other directions, led to increased peak head accelerations and shorter SCM onset latencies.
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 | 0.001 | 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) | 0.000 | 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".