Table 7.

Mixing types of distraction detection algorithms.

Algorithm	Features	Classifier	Average Accuracy (%)
Li et al. [194]	AU and head pose	LDC (visual distraction) and SVM (cognitive distraction)	80.8 (LDC), 73.8 (SVM)
Craye et al. [195]	eye behaviour, arm position, head orientation and facial expressions using both color and depth images	Adaboot and HMM	89.84 (Adaboot), 89.64 (HMM)
Liu et al. [196]	Head and eye movements	SVM, ELM and CR-ELM	85.65 (SVM), 85.98 (ELM), 86.95 (CR-ELM)
Ragab et al. [197]	arm position, eye closure, eye gaze, facial expressions and head orientation using depth images	Adaboost, HMM, RF, SVM, CRF, NN	82.9 (RF—type of distraction detection), 90 (RF—distraction detection)