EmbodiedRecall: A Ring-to-Glasses System for Preserving Valuable, Fleeting Moments in Daily Activities

Capturing meaningful everyday moments is challenging as they often occur when users are cognitively absorbed or physically occupied. While always-on recording reduces “misses,” it introduces severe privacy concerns and data triage fatigue. We present EmbodiedRecall, a ring-to-glasses system that operationalizes a proactive capture paradigm. By continuously analyzing fine-grained hand dynamics via dual IMU-equipped rings, the system recognizes compositional motion primitives and utilizes a multimodal Large Language Model (MLLM) to infer capture intent within hand-anchorable embodied moments. To balance agency with automation, EmbodiedRecall delivers subtle haptic or visual prompts, allowing users to “confirm-to-commit” short video clips from a privacy-preserving rolling buffer. A controlled lab study ( N = 20) validated our multi-stage pipeline, showing that our model significantly outperforms deep learning baselines with 75.35% accuracy. A 6-day active-session deployment ( N = 13) demonstrated that the system correctly aligns with user intent (46.5% confirmation rate) while surfacing serendipitous, emotionally resonant “micro-moments” users would not have captured manually. We discuss design implications for predictive wearables that enhance socio-emotional preservation while maintaining user agency.