Classification of driving data using labels derived from psychophysiological data achieved a maximum AUC of 74%, equivalent to the 73% AUC achieved using subjective self-report labels.
Observational (n=21)
Does labelling driving data via psychophysiological activity improve the classification accuracy of negative emotions compared to subjective self-report in healthy drivers?
Dynamically labelling driving data using psychophysiological measures like heart rate achieves equivalent classification accuracy for negative emotions (AUC 74%) compared to subjective self-reports (AUC 73%), offering an objective, high-fidelity alternative.
Absolute Event Rate: 74% vs 73%
Driving is an activity that can induce significant levels of negative emotion, such as stress and anger. These negative emotions occur naturally in everyday life, but frequent episodes can be detrimental to cardiovascular health in the long term. The development of monitoring systems to detect negative emotions often rely on labels derived from subjective self-report. However, this approach is burdensome, intrusive, low fidelity (i.e. scales are administered infrequently) and places huge reliance on the veracity of subjective self-report. This paper explores an alternative approach that provides greater fidelity by using psychophysiological data (e.g. heart rate) to dynamically label data derived from the driving task (e.g. speed, road type). A number of different techniques for generating labels for machine learning were compared: 1) deriving labels from subjective self-report and 2) labelling data via psychophysiological activity (e.g. heart rate (HR), pulse transit time (PTT), etc.) to create dynamic labels of high vs. low anxiety for each participant. The classification accuracy associated with both labelling techniques was evaluated using Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). Results indicated that classification of driving data using subjective labelled data (1) achieved a maximum AUC of 73%, whilst the labels derived from psychophysiological data (2) achieved equivalent performance of 74%. Whilst classification performance was similar, labelling driving data via psychophysiology offers a number of advantages over self-reports, e.g. implicit, dynamic, objective, high fidelity.
Dobbins et al. (2018) conducted an observational in Negative emotions (stress/anger) during driving (n=21). Dynamically labelling driving data via psychophysiological activity (e.g., heart rate) vs. Subjective self-report labels was evaluated on Classification accuracy (Area Under the Curve) for detecting negative emotions. Classification of driving data using labels derived from psychophysiological data achieved a maximum AUC of 74%, equivalent to the 73% AUC achieved using subjective self-report labels.