Integrated Sensor-Based Smart Mannequin for Injury Detection in Armored Vehicle
Bibliographic record
Abstract
In the pursuit of developing armored vehicles that offer superior safety and performance across challenging terrains, the accurate assessment of driver and passenger injury levels is critical.Currently, safety testing heavily relies on the subjective expertise of a limited number of officers.To address this limitation, we present a novel approach using a smart mannequin embedded with advanced sensor systems, emulating human-like perception.The mannequin incorporates various sensors including accelerometers, temperature sensors, as well as gas, sound, and camera sensors.Leveraging the Raspberry Pi 4B and Node MCU, we employ Internet of Things (IoT) technology to enable real-time monitoring of driver and passenger conditions within the vehicle through a web-based interface.Rigorous laboratory and field experiments were conducted to evaluate the system's performance.Our findings demonstrate the efficacy of the proposed system in monitoring smart mannequins via web applications.The alert system successfully detects gas leaks, sounds, vibrations, temperature fluctuations, and humidity levels, while also providing valuable data on speed, vibration, and position using accelerometers and GPS.Empowering smart mannequins to assume the role of humans in conducting risky tests presents a significant advancement in vehicle safety testing.
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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 | 0.000 |
| 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 | 0.001 | 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".