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This article explores the influence of personality on physiological data while driving in reaction to near crashes and risky situations using Machine Learning (ML). The objective is to improve the driving assistance systems in considering drivers’ characteristics. Methods:: Physiological and behavioral data were recorded in sixty-three healthy volunteers during risky urban situations and analyzed using 5 ML algorithms to discriminate the driver’s personality according to Big Five Inventory and STAI trait. Seven step process was performed including data pre-processing, Electrodermal Activity (EDA) time windows selection (one by one backward and forward approach comparison with a pseudo-wrapped), personality traits assessment, input algorithms parameters optimization, algorithm comparison and personality trait cluster prediction. ROC Area Under the Curve (AUC) was used to describe improvement. Results/discussion:: The pseudo-wrapped/all possibilities method comparison resulted in 8.3% on average for all personality traits and all algorithms (% of ROC AUC of backward and forward approach). The ROC AUC for the detection of the personality ranged between 0.968 to 0.974 with better detection of Openness, Agreeability and Neuroticism. Use of association between Neuroticism, Extraversion and Conscientiousness previously defined in the literature slightly improve personality detection (maximum ROC AUC of 0.961 to 0.993 for cluster). Results are discussed in terms of contribution to driving aids. Conclusion:: This study is one of the first to use machine learning techniques to detect personality traits using behavioral and physiological measures in a driving context. Additionally, it questions input parameters optimization approach, time windows selection, as well as clustering and association of personality trait for detection improvement.

Background: Walking while performing a secondary task (dual-task (DT) walking) increases cognitive workload in young adults. To date, few studies have used neurophysiological measures in combination to subjective measures to assess cognitive workload during a walking task. This combined approach can provide more insights into the amount of cognitive resources in relation with the perceived mental effort involving in a walking task.Research Question: The objective was to examine cognitive workload in young adults during walking conditions varying in complexity.Methods: Twenty-five young adults (mean = 24.4 ± 5.4) performed four conditions: (1) usual walking, (2) simple DT walking, (3) complex DT walking and (4) standing while subtracting. During the walking task, mean speed, cadence, stride time, stride length, and their respective coefficient of variation (CV) were recorded. Cognitive workload will be measured through changes in oxy- and deoxy-hemoglobin (ΔHbO2 and ΔHbR) during walking in the dorsolateral prefrontal cortex (DLPFC) and perceived mental demand score from NASA-TLX questionnaire.Results: In young adults, ΔHbO2 in the DLPFC increased from usual walking to both DT walking conditions and standing while subtracting condition. ΔHbO2 did not differ between the simple and complex DT and between the complex DT and standing while subtracting condition. Perceived mental demand gradually increased with walking task complexity. As expected, all mean values of gait parameters were altered according to task complexity. CV of speed, cadence and stride time were significantly higher during DT walking conditions than during usual walking whereas CV of stride length was only higher during complex DT walking than during usual walking.Significance: Young adults had greater cognitive workload in the two DT walking conditions compared to usual walking. However, only the mental demand score from NASA-TLX questionnaire discriminated simple from complex DT walking. Subjective measure provides complementary information to objective one on changes in cognitive workload during challenging walking tasks in young adults. These results may be useful to improve our understanding of cognitive workload during walking.