Figuring out Distraction Degree from Working Memory Consumption for Pedestrian Safety

This paper proposes a method to estimate the distraction degree of 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 point out distraction of the pedestrian relates to consumption of working memory. We assume considering the relationship between consumption of working memory and walking behavior suggests the effectiveness to estimate distraction of the pedestrian. When each pedestrian is walking while consuming working memory, for example thinking about something, their walk changes. A machine Learning method, Random Forest, is applied to classify whether the pedestrian is distracted using features of walking. An experiment suggests the method estimates the distraction degree of the pedestrian with 20% error. The result indicates the method can find distracted pedestrians whose working memory is consumed. We discuss why we can estimate the distraction degree of the pedestrian from walking feature components with the variable importance. We found some features appeared in the middle and high frequency bands. Finally, we discuss the feasibility of our proposed method.