The problem Eye Blink Detection Module solves

I was able to successfully:

1. Differentiate between a normal blink and a tired eye closure by keeping a note of the frequency and duration of these blinks.
2. Find an optimal value for the number and duration of these eye blinks which helps us to have an accurate measure of when the person starts feeling drowsy.

PS: This module can act as a good fit to be integrated to several large-scale and can be modified as per the need.

Challenges I ran into

The challenge with this module was to have an accurate measure of the number of eye-blinks and the frequency of these blinks which would help me differentiate between a normal eye closure and a tired eye closure
This challenge was overcome with continuous trial and error of the eye-blink detection module on a group of people. The threshold value for each one of them was recorded and a cumulative measure suggested to me the value which should be finally taken for this module.