Detecting Nicotine Addiction via Eye Tracking

Abstract

Smoking addiction is a growing social epidemic affecting about 1 in 5 adults in the world directly and many more in the form of passive smoking. Diagnostic tools to determine the nicotine addiction are either unreliable or restrictive in their use. This makes identification and assessment less accessible for those who need it. We attempted to evaluate eye tracking paired with machine learning as an alternative to existing diagnostic tools for assessing nicotine dependence.