Integrated Sensor-Based Smart Mannequin for Injury Detection in Armored Vehicle
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 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) | 0.000 | 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 it