AI-Powered Woman Safety Application with Real-Time Audio-Based Trigger and Emergency Alert System
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
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.
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.
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 | 0.002 | 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 | 0.001 | 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) | 0.000 | 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