Combining heart and breathing rate for car driver stress recognition

This paper investigates the use of heart rate (HR) and breathing rate (BR) signals for the automatic recognition of car driver stress. The problem is defined as a binary classification problem, that is stress vs no-stress, and it is tackled by using the Support Vector Machines classification strategy. We propose the use of a combination of traditional state of the art features and raw values of the acquired signals as data representation. Experimentation is carried out on a subset of data belonging to a publicly available dataset of simulated driving. The HR and BR signals are acquired with an adrenergic sensor connected to a chest strap that is worn underneath the driver's clothing. The experiments are made considering the leave-one-subject-out configuration, that is the subject under test is not included in the training set. The goodness of HR and BR is tested separately and then a combination of them. The results obtained are very encouraging, especially considering that the leave-one-subject-out configuration is adopted. The results reach a mean average accuracy of about 70% in the case where the combination of HR and BR is adopted.