  Non-invasive devices involved in the detection of drowsiness generally
include infrared camera and Electroencephalography (EEG), of which sometimes
are constrained in an actual real-life scenario deployments and implementations
such as in the working office environment. This study proposes a combination
using the biometric features of keyboard and mouse movements and eye tracking
during an office-based tasks to detect and evaluate drowsiness according to the
self-report Karolinska sleepiness scale (KSS) questionnaire. Using machine
learning models, the results demonstrate a correlation between the predicted
KSS from the biometrics and the actual KSS from the user input, indicating the
feasibility of evaluating the office workers' drowsiness level of the proposed
approach.
