Smart Handlebar with Integrated Auto Finger Sensor for Biometric Authentication and Rider Safety
Bibliographic record
Abstract
This work proposes a dual-layer access control and health monitoring framework designed for smart mobility applications such as electric bicycles. The system integrates fingerprint-based biometric authentication with real-time physiological monitoring to enhance both security and rider safety. Fingerprint input is used to authorize legitimate users, while health parameters including blood pressure, oxygen saturation$\left(\text{SpO}_{2}\right)$, and body temperature are continuously assessed to verify the rider's fitness to operate the vehicle. A MATLAB/Simulink model was developed to simulate the decision-making process, where logical operators merge authentication results with health thresholds to determine access permission. Hardware implementation using a fingerprint sensor, pulse oximeter, and temperature sensor validates the proposed approach. Experimental results show an authentication success rate of 95 %, a false acceptance rate below 1 %, and health parameter accuracy within acceptable medical tolerance. Emergency alerts via GSM/GPS were triggered within 15 seconds during abnormal conditions. The fingerprint identification module eliminates the risk of theft and misuse, augmenting accountability as well as user identification. Concurrently, the embedded medical monitoring sensors provide ongoing feedback to the onboard system, which can alert or invoke emergency protocols in the event of abnormal readings such as signs of an oncoming heart attack or severe fatigue. These dual-use not only augment personal security but also ensure public security by preventing accident risks caused by medical crises during traveling.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".