Novelty Based Driver Identification on RR Intervals from ECG Data

Abstract

We present an approach for driver identification, which is useful in many automotive applications such as safety or comfort functions. Driver identification would also be of great interest to other business models, such as car rental and car-sharing companies. The identification method is based on the driver’s physiological state or rather his/her electrocardiogram (ECG) data. For this purpose, we have recorded ECG data of 25 people driving in a simulated environment. To identify a driver, we extend our existing novelty detection by aggregating local features over time. To do so, we extracted features and trained a Gaussian Mixture Model (GMM) to exploit localities present in the recorded sensor data. With novelty detection by aggregating local features, we are smoothing the noisy signal and reducing the dimensionality for further processing in a one-class SVM classification. Based on the output, a decision function decides whether the driver is unknown or well-known and if the driver is well-known, who of the known driver is it.

The project ”VitaB - Klassifizierung der Vtalparameter zur Individuellen vitalen und kognitiven Zustandsbestimmung des Menschen” (HA project no. 545/17–27) is financed with funds of LOEWE – Landes-Offensive zur Entwicklung Wissenschaftlich-ökonomischer Exzellenz, Förderlinie 3: KMU-Verbundvorhaben (State Offensive for the Development of Scientific and Economic Excellence).