Investigation of Brain Response in Canadian Armed Forces Volunteers Subjected to Recoil Force from Firing Long-Range Rifles Using Instrumented Mouthguards and Finite Element Head Model
Bibliographic record
Abstract
Mild traumatic brain injury (mTBI) may be caused by occupational hazards military personnel encounter, such as falls, shocks, exposure to blast overpressure events, and recoil from weapon firing. The repeated exposure of Canadian Armed Forces (CAF) members to sub-concussive events during the course of their service may lead to a significant reduction in quality of life. Symptoms may include headaches, difficulty concentrating, and noise sensitivity, impacting how personnel complete their duties and causing chronic health issues. CAF members have reported experiencing symptoms of mTBI, and some studies have associated these symptoms with repeated firing of long-range rifles. However, there is limited physical data on head response resulting from rifle recoil and different rifle configurations. The objectives of this study were to quantify head kinematics for volunteers, assess the head response using kinematic-based metrics, and assess brain response using a detailed finite element head model. Measurements of head motion were recorded in a group of military volunteers using instrumented mouthguards while firing long-range rifles. The head kinematics were then used as inputs in a finite element head model to calculate the brain strains for each firing event and assessed using common response metrics and a Cumulative Strain Volume (CSV) measure to quantify brain deformation resulting from head acceleration. The measured head kinematics and predicted brain deformation among CAF volunteers were lower than those associated with acute injury. The study highlighted the corpus callosum as the primary site of higher strains in the brain, consistent with previous research on head response to acceleration events. Brain deformation was primarily associated with angular velocity rather than linear acceleration. Comparative analysis between different rifle calibers revealed higher values of head kinematics associated with increased rifle caliber, owing to the higher level of energy. The CSV method identified statistically significant differences between rifle configurations and reductions in brain deformation with a recoil mitigation system (RMS), offering a potential solution to reduce long-term symptoms from firing long-range rifles. The results of this study offer important information about the magnitude of kinematics and strains that volunteers experience when firing long-range rifles.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".