Recognizing situation awareness of forklift operators based on eye-movement & EEG features

Lack of situation awareness (SA) is a major source of human error in forklift operations. Effective assessment of SA levels is a critical link in improving the SA of forklift operators. Aiming at the intrusive, subjective and intermittent problems of current measurement methods, this paper proposed a SA recognition method based on eye movement and electroencephalography (EEG) features. A forklift operation experiment was designed in a real-life scenario, where eye movement and EEG data of forklift operators were collected, and the Situation Awareness Rating Technique (SART) method was used to calculate SA scores. Independent sample t-test and Mann-Whitney test were used to investigate the differences in eye movement and EEG indicators among participants with different SA levels. Finally, the classification models of K-Nearest neighbor (KNN), Random Forest (RF) and Support Vector Machine (SVM) were used to recognize the SA levels of forklift operators. The results indicated that the visiting time indicators, fixation time indicators and fixation count indicators in particular Areas of Interest (AOIs) are significantly different from the SA levels; The combined EEG indicators θ/β, (α+θ)/(α+β), θ/(α+β) in Frontal (F) lobe, Parietal (P) lobe and Central (C) lobe, and (α+θ)/β in P lobe and C lobe are significantly different from the SA levels; The average recognition accuracies of the models of KNN, RF and SVM are 90.61%, 94.18% and 91.15%, respectively, with the RF model having the highest recognition accuracy. The results confirmed that the method can be used to assess the SA of forklift operators in the real environment, which provides a new avenue for SA measurement.