Estimating Distracted Pedestrian from Deviated Walking Considering Consumption of Working Memory

This paper proposes a method to distinguish distracted pedestrians from normal pedestrians, using the acceleration and the angular velocity while walking. This method uses an acceleration sensor attached on the back of the pedestrian. The acceleration and the angular velocity are obtained while the pedestrian is walking. In addition to that, walking features are calculated based on the obtained data. Some studies points out distraction of the pedestrian relates to consumption of working memory. We assume considering the relationship between consumption of working memory and walking behavior suggest the effectiveness to estimate distraction of the pedestrian. When each pedestrian is walking while consuming working memory, for example thinking about something, their walk deviates from normal. Machine Learning method, Random Forest, is applied to classify whether the pedestrian is distracted using features of walking. An experiment suggests we can distinguish walking features represent both distracted state and normal state completely. The result indicates the method can find distracted pedestrians whose working memory is highly consumed. We discuss why we can distinguish the distraction of the pedestrian from walking feature components with the variable importance. In addition, we have conducted regression analysis on the significant feature components to figure out the reasons. Finally, we discuss the feasibility of our proposed method.