Feasibility of an AI-Enabled Smart Mirror Integrating MA-rPPG, Facial Affect, and Conversational Guidance in Realtime

Abstract

This paper presents a real-time smart mirror system combining multiple AI modules for multimodal health monitoring. The proposed platform integrates three core components: facial expression analysis, remote photoplethysmography (rPPG), and conversational AI. A key innovation lies in transforming the Moving Average rPPG (MA-rPPG) model-originally developed for offline batch processing-into a real-time, continuously streaming setup, enabling seamless heart rate and peripheral oxygen saturation (SpO2) monitoring using standard webcams. The system also incorporates the DeepFace facial analysis library for live emotion, age detection, and a Generative Pre-trained Transformer 4o (GPT-4o)-based mental health chatbot with bilingual (English/Korean) support and voice synthesis. Embedded into a touchscreen mirror with Graphical User Interface (GUI), this solution delivers ambient, low-interruption interaction and real-time user feedback. By unifying these AI modules within an interactive smart mirror, our findings demonstrate the feasibility of integrating multimodal sensing (rPPG, affect detection) and conversational AI into a real-time smart mirror platform. This system is presented as a feasibility-stage prototype to promote real-time health awareness and empathetic feedback. The physiological validation was limited to a single subject, and the user evaluation constituted only a small formative assessment; therefore, results should be interpreted strictly as preliminary feasibility evidence. The system is not intended to provide clinical diagnosis or generalizable accuracy at this stage.