Quantification of Behind Shield Blunt Impacts Using a Modified Upper Extremity Anthropomorphic Test Device
Bibliographic record
Abstract
Ballistic shields are used by military and police members in dangerous situations to protect the user against threats such as gunfire. When struck, the shield material deforms to absorb the incoming kinetic energy of the projectile. If the rapid back-face deformation contacts the arm, it can potentially impart a large force, leading to injury risk, termed behind armor blunt trauma (BABT). This work characterized the loading profiles due to the contact between the deforming back-face of the shield and the arm using a modified upper extremity anthropomorphic test device (ATD). This ATD measured forces at the hand, wrist, forearm, and elbow to compare the locational effects of the force transfer for future investigations of fracture risk. Two composite ballistic shields, both with the same ballistic protection rating, were investigated and had statistically different responses to the same impact conditions, indicating a further need for shield safety evaluation. Additionally, ballistic force curves were compared among stand-off distances, defined as the distance between the back-face of the shield and the front of the force sensor, where the peak impact force significantly decreased with increased stand-off. This study presents the first highly instrumented ATD upper limb capable of evaluating BABT and characterization of these loading profiles. This work demonstrates the importance of realistic boundary conditions as loading varies by anatomical location. Stand-off distance is an effective method to reduce loading and should be considered in future shield design iterations and standards that are developed using this device.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".