AI That Cares: Building an Emotion and Fatigue Detector
In my journey as a developer in Maiduguri, I’ve always been fascinated by how machines can understand human states. My project, emotion-fatigue-detector_app, was born from a simple question: Can AI prevent accidents by knowing when we are tired?
The Core Technology
To build this, I focused on three main technical pillars:
- Facial Landmark Detection: Identifying key points around the eyes and mouth.
- Eye Aspect Ratio (EAR): A mathematical formula to detect if eyes are closing for too long (indicating drowsiness).
- CNN Models: Using Convolutional Neural Networks to classify emotions like stress or exhaustion.
Why This Matters
Whether it’s a long-distance driver or a software engineer working late on Ubuntu 24.04, fatigue is a silent risk. By integrating this into mobile and desktop apps, we can create a “safety net” that alerts users before mistakes happen.
Current Progress
Right now, the app can successfully:
- Detect eye closure patterns in real-time.
- Differentiate between a simple blink and a fatigue-related “micro-sleep.”
- Categorize 5 basic emotional states.
Check out the code here: emotion-fatigue-detector_app
What’s next? I’m working on optimizing the frame rate for lower-end mobile devices to ensure this safety tool is accessible to everyone.
🚀 More technical deep-dives coming soon!