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.