This research employs statistical methods, such as hierarchical Bayesian modeling, to examine how features in physiological and behavioral data relate to human functional states. Current data are used to quantify individual differences and assess the extent to which future changes and adaptability can be predicted. The aim is to apply these approaches to technologies that can detect early signs of changes in health and physical function.

Assessing physiological and physical functions often requires specialized equipment and complex measurements. At the same time, there is a growing need for simple and accessible methods for monitoring physical function in everyday life. This research focuses on developing mobile applications that record and analyze physiological and behavioral data, such as sound and movement, for functional assessment. The aim is to use everyday data as indicators for continuously monitoring changes in health and physical function.