Cycle tourism is increasingly recognized as a means of regional revitalization worldwide. To support this trend, planning suitable cycling routes is essential. Especially, routes considering narrative, i.e., defined as the thematic and emotional coherence that links tourist spots into a meaningful story, are preferred by cycle tourists. However, traditional methods prioritize only physical intensity and route distances. To address this gap, we propose an AI-based system that generates narrative-rich cycling routes. Our proposed system gives the emotional features extracted from Points of Interest as narrative quality. Our evaluation revealed that our approach improved the narrative-aware route quality from 1.561 to 1.726 (increased by 10.6%).

Due to the recent popularity of sports bicycles, many people have started tourism with cycling. Beginner cyclists often try to complete long distance cycling without sufficient training, causing overuse of leg muscles. Overuse of leg muscles caused by an irregular bicycle riding form leads to diseases such as iliotibial band syndrome. It is helpful to automatically identify beginner cyclists of irregular forms to avoid these diseases. However, none of the multimodal AI models recently attracting attention can achieve this. This paper proposes a system architecture for detecting individually optimized irregular bicycle riding form. Our proposed architecture uses bicycles installed with the Inertial Measurement Unit (IMU) and 2D Light Detection and Ranging (2D LiDAR) sensors. The sensor data obtained from IMU are analyzed by Recurrent Neural Network-based models, and the results are refined by multimodal classifier models combined with the 2D LiDAR data. The model selected by the proposed method obtained 0.900 in accuracy, 0.892 in precision, and 0.841 in recall for an f-score of 0.862, demonstrating the effectiveness of meta-model approaches.

In the context of open science, the management of metadata is essential for promoting research data utilization. Experimental scientists are required to manage a substantial volume of experimental data, including a significant proportion of failed data. This places a considerable burden on the experimental scientists. In this paper, we outline the development of a conceptual image for a data aggregation and management infrastructure for core facilities. The infrastructure enables an automatic assignment of unique identifier and metadata, optimizing research data management of experimental scientists.