Event-related driver stress detection with smartphones in an urban environment: a naturalistic driving study
开发了基于智能手机的数据采集协议，在自然驾驶中收集61名驾驶员的生理、心理和面部数据，识别并分类压力事件，最佳模型以92.5%的准确率预测低/中/高压力事件，可用于可视化压力源分布和监测驾驶行为。
Driving in urban areas can be challenging and encounter acute stress. To detect driver stress, collecting data on real roads without interfering the driver is preferred. A smartphone-based data collection protocol was developed to support a naturalistic driving study. Sixty-one participants drove on predetermined real road routes, and driving information as well as physiological, psychological, and facial data were collected. The algorithm identified potentially stressful events based on the collected data. Participants classified these events as low, medium, or highly stressful events by watching recorded videos after the experiment. These events were then used to train prediction models. The best model achieved an accuracy of 92.5% in classifying low/medium/highly stressful events. The contribution of physiological, psychological, and facial expression indices and individual profile information was evaluated. The method can be applied to visualise the geographical distribution of stressors, monitor driver behaviour, and help drivers regulate their driving habits.