AI-Powered Woman Safety Application with Real-Time Audio-Based Trigger and Emergency Alert System
Bibliographic record
Abstract
The AI-Driven Female Safety App will be a real-time emergency reporting system, that will be voice initiated and will help support the needs of damaged/unsafe individuals in an unsecure or compromised situation. This solution incorporates Artificial Intelligence (AI) to recognize a list of pre-defined emergency keywords eg; "help," "save me" etc., through an Android smartphones continual background listening ability. This app will be designed to continually listen for these keywords and upon recognition will go through a sequence of automated actions including, haring live location through GPS, recording audio, turning on a siren noise, turning on the flash light, and then automatically notifying the required emergency contacts. This type of tool will allow recordings to operate entirely autonomously and passively - without any action or engagement from the Ontario woman. As above, if a woman is unconscious, paralyzed or restrained, this type of app can be very beneficial. The app will use an efficient, lean model of AI dedicated to keyword spotting that will rely on already established smartphone sensor APIs for microphones, GPS, and flashlight. Not only is the app being designed to detect an emergency accurately and reliably, it will also address concepts of false positives, privacy, and power consumption by utilizing a modular design, with configured configurability thresholds. This step relies on utilizing existing technology that can provide not only personal safety and security (that will become increasingly important for women), but provide a low-cost, scalable, user-friendly safety tool that is using any modern installed version of the Android device and the sensors already included, and not extra hardware from another device.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.015 |
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".