# Explain detections

Model reasoning

##### How detections are made

This is explanatory text for operators and governance:

- PoseNet tracks the driver’s skeleton (head, shoulders, torso) at each frame.
- OpenCV tracks face and eyes to compute eye aspect ratio and blink metrics.
- Signals are combined over short time windows (e.g. 3–10 seconds) to classify fatigue events.

Examples

- Microsleep: very low eye aspect ratio + minimal head movement for multiple seconds.
- Eye-closure risk: reduced blink frequency + longer-than-normal closures.
- Pose-only risk: sustained slouching or head tilt beyond configured degrees.

##### Sample explanation template

Use a structure like this when wiring your own AI:

1. What did the model see? (inputs, time window, raw signals).
2. How did it combine them? (thresholds, weights, risk scoring).
3. Why this label? (microsleep vs eye-closure vs pose-only).
4. What should the operator do? (clear next steps).

Example natural language explanation

“Between 03:12:14 and 03:12:17, the driver’s eyes remained closed while the vehicle was moving at highway speed. Blink frequency dropped to nearly zero compared with the previous minute, and head pose was stable instead of actively scanning mirrors. Taken together, these signals match our microsleep pattern, so the model raised a critical fatigue alert.”

Prompt idea for the AI

`“Explain in plain language why this event was labeled as a microsleep, which input signals it relied on (PoseNet, OpenCV), and what the recommended operator response should be.”`