2 research outputs found
A categorisation and implementation of digital pen features for behaviour characterisation
In this paper we provide a categorisation and implementation of digital ink
features for behaviour characterisation. Based on four feature sets taken from
literature, we provide a categorisation in different classes of syntactic and
semantic features. We implemented a publicly available framework to calculate
these features and show its deployment in the use case of analysing cognitive
assessments performed using a digital pen
Heatmap-Based Method for Estimating Drivers' Cognitive Distraction
In order to increase road safety, among the visual and manual distractions,
modern intelligent vehicles need also to detect cognitive distracted driving
(i.e., the drivers mind wandering). In this study, the influence of cognitive
processes on the drivers gaze behavior is explored. A novel image-based
representation of the driver's eye-gaze dispersion is proposed to estimate
cognitive distraction. Data are collected on open highway roads, with a
tailored protocol to create cognitive distraction. The visual difference of
created shapes shows that a driver explores a wider area in neutral driving
compared to distracted driving. Thus, support vector machine (SVM)-based
classifiers are trained, and 85.2% of accuracy is achieved for a two-class
problem, even with a small dataset. Thus, the proposed method has the
discriminative power to recognize cognitive distraction using gaze information.
Finally, this work details how this image-based representation could be useful
for other cases of distracted driving detection.Comment: Accepted at IEEE ICCI*CC 2020 (matching camera-ready version