In this paper, we propose a MiRRoR(Mixed-Reality of Robust Rendering) that enables user localization in the mobility scenario. Mixed Reality (MR) is an emerging technology for interacting with spatial digital content. It merges virtual content and the actual world through the camera. Nowadays, many kinds of Head-Mount Display (HMD) devices for MR have been released, and they can be more popular in the future. However, MR is not available in mobility situations because of localization problems. This research aims to solve this problem using a sensor fusion approach. We developed an MR bus tour with visual-based localization and GPS, then conducted a preliminary experiment to evaluate localization accuracy and MR experience.

This study presents SatisFace, a novel approach to measuring customer satisfaction in amusement parks by analyzing facial expressions at entry and exit points. Recognizing satisfaction's subjectivity, SatisFace assesses facial Action Unit (AU) changes, tailoring assessments to individual user characteristics. Data were collected through an experiment with 200 participants at Soleil Hill, using questionnaires and OpenFace for facial analysis. A gradient-boosting machine learning model revealed a significant correlation between changes in facial expressions and satisfaction levels.

This study aims to develop a heat stroke risk estimation system that takes into account not only environmental information but also internal factors of individuals in order to prevent the increasing number of heat stroke deaths in Japan. 26 outdoor workers were subjected to comprehensive measurements of environmental and biological data between August 15 and September 17, 2023, A prediction model was developed using a random forest classifier. While the model is able to predict with high accuracy cases with no risk, there is room for improvement in the detection of risky cases. The system aims to personalize heat stroke prevention measures to achieve more effective risk management.

Crowdedness sensing of buses is playing an important role in the disease control of COVID-19 and bus resource scheduling. This research analyzes the relationship between carbon dioxide concentration, bus environment and the number of passengers by linear regression. Our prototype system collects the data of bus environment and carbon dioxide concentration to estimate the number of passengers in real time. By collecting the sensing data from a shuttle bus of university campus, we experimentally evaluate the feasibility and sensing performance of the crowdedness estimation model.