It becomes possible to prevent accidents beforehand by predicting dangerous riding behavior based on recognition of bicycle behaviors. In this paper, we propose a bicycle behavior recognition method using a three-axis acceleration sensor and three-axis gyro sensor equipped with a smartphone when it is installed on a bicycle handlebar. We focus on the periodic handlebar motions for balancing while running a bicycle and reduce the sensor noises caused by them. After that, we use machine learning for recognizing the bicycle behaviors, effectively utilizing the motion features in bicycle behavior recognition. The experimental results demonstrate that the proposed method accurately recognizes the four bicycle behaviors of stop, run straight, turn right, and turn left and its F-measure becomes around 0.9. The results indicate that, even if the smartphone is installed on the noisy bicycle handlebar, our proposed method can recognize the bicycle behaviors with almost the same accuracy as the one when a smartphone is installed on a rear axle of a bicycle on which the handlebar motion noises can be much reduced.

Due to the recent development of underlying hardware technology and improvement in installing environments, public display has been becoming more common and attracting more attention as a new type of signage. Any signage is required to make its content more attractive to its viewers by evaluating the current attractiveness on the fly, in order to deliver the message from the sender more effectively. However, most previous methods for public display require time to reflect the viewers' evaluations. In this paper, we present a novel system, called Mood-Learning Public Display, which automatically adapts its content design. This system utilizes viewers' involuntary behaviors as a sign of evaluation to make the content design more adapted to local viewers' tastes evolutionarily on site. The system removes the current gap between viewers' expectations and the content actually displayed on the display, and makes efficient mutual transmission of information between the cyberworld and the reality.