Abstract
Recent advances in ubiquitous sensing technologies have enabled systems to recognize and support daily human activities for behavioral change interventions. While previous research has explored activity recognition in various healthcare domains, automated monitoring and support systems for daily skin care activities remain understudied. This paper presents a novel system that combines continuous sensor monitoring of skin conditions with personalized behavior change support. The system employs precision sensors to capture fine-grained temporal changes in skin moisture and sebum levels, enabling detailed activity recognition of users’ skin care routines and their effects. Based on the collected multimodal sensor data and identified behavioral patterns, the system implements an adaptive intervention mechanism that provides personalized recommendations aligned with individual skin types and care activities. Our research aims to promote sustainable behavior changes in skin care routines by helping users objectively understand the relationship between their daily activities and skin condition variations. Through experimental evaluation, we demonstrate how the system effectively recognizes skin care activities and influences user behavior for improved skin health maintenance. This work contributes to expanding activity recognition and behavior computing applications in the personal care domain.
https://ror.org/01pa62v70 
