The PPB-Emo dataset will largely support the analysis of human emotion in driving tasks. In Experiment III, 40 participants were recruited, and the psychological data and physiological data, as well as their behavioural data were collected of all participants in 280 times driving tasks.
Euro truck driver download drivers#
For Experiment II, 409 participants were recruited, a questionnaire survey was conducted to obtain driving scenarios information that induces human drivers to produce specific emotions, and the results were used as the basis for selecting video-audio stimulus materials. In Experiment I, 27 participants were recruited, the in-depth interview method was employed to explore the driver’s viewpoints on driving scenarios that induce different emotions. we conducted three experiments to collect multimodal psychological, physiological and behavioural dataset for human emotions (PPB-Emo). Creating a multi-modal human emotion dataset in driving tasks is an essential step in human emotion studies. Human emotions are integral to daily tasks, and driving is now a typical daily task. Sensing platform development, performance, and limitations, as well as other potential applications, are discussed in detail in this paper. A cloud-based communication scheme was developed for the ease of data collection and analysis. This flexible sensor was mounted on an off-the-shelf steering wheel sleeve, making it an add-on system that can be installed on any existing vehicles for convenient and wide-coverage driver monitoring. The force-sensing unit (FSU) for hand pressure detection has a range of 25 N. The EMG signal acquired by the EMG-sensing unit (EMGSU) was amplified to within 5 V. The sweat-sensing unit (SSU) for EDA monitoring works under a 100 Hz alternative current (AC) source. Comprehensive characterizations on the sensing modalities were performed with promising results demonstrated. The sensor suite was developed by combining low-cost interdigitated electrodes with a piezoresistive force sensor on a single, flexible polymer substrate. In this paper, we present a novel multi-modal smart steering sleeve (S3) system with an integrated sensing platform that can non-intrusively and continuously measure a driver’s physiological signals, including electrodermal activity (EDA), electromyography (EMG), and hand pressure. Such information can then be utilized for enhanced assistive vehicle controls and/or driver health monitoring. Therefore, driving can be used as a controlled environment for the frequent, non-intrusive monitoring of bio-physical and cognitive status within drivers. These aspects become more problematic for some seniors, who have underlining medical conditions and tend to lose some of these capabilities. Driving is a ubiquitous activity that requires both motor skills and cognitive focus.
0 kommentar(er)
