Fatigue measurement has been undertaken using physiological measurements, but a non-invasive way of accurate detection of fatigue remains under investigation. Alter is a video-based embedded solution for detecting drivers’ signs of fatigue and tendencies to fall asleep during driving in real-time. It works by capturing and analyzing critical features related to sleep and drowsiness over short but regular time intervals. These include micro-sleeps, eye-blinking, yawing opening, as well as head tilts and orientation. Using deep learning, the system has been trained to extract those features using a reference and well-established data sets of videos produced by subjects at various levels of fatigue tagged according to KSS (Karolinska Sleepiness Scale). A machine learning-model was then used to classify new instances of driving videos based the extracted parameters. The model can be run on the cloud but a new embedded model is under development on high-speed edge computing portable device that can be mounted on the car dashboard.