Researchers at Korea Advanced Institute of Science and Technology, in collaboration with Sungkyunkwan University and Korea University Anam Hospital, have developed an artificial intelligence framework capable of identifying early warning signs of cerebrovascular disease through continuous monitoring of daily life patterns. Published in npj Digital Medicine, the study introduces a predictive tool designed to shift cerebrovascular care from reactive treatment to proactive intervention by analyzing domestic behavioral and environmental data. The AI system was trained on extensive lifelog datasets provided by LivOn Care Co., Ltd., encompassing real-time residential monitoring of 1,224 older adults. Researchers processed over 13,000 two-week data samples, evaluating metrics such as daily activity, sleep architecture, circadian rhythms, indoor climate conditions, age, and preexisting chronic conditions. By focusing on subtle, longitudinal deviations from established baselines, the model identifies prodromal markers that typically evade conventional clinical screenings. A central achievement of the framework is its capacity to distinguish between imminent diagnostic risk and standard baseline conditions with 96.53 percent accuracy. When comparing behavioral data recorded within four weeks prior to clinical diagnosis against samples from twelve weeks out, the AI successfully flagged impending health deterioration. The system employs explainable AI architecture, ensuring that its predictions are traceable to specific lifestyle and environmental variables rather than operating as a black box. Detailed analysis revealed distinct behavioral shifts associated with cerebrovascular risk. During the prodromal phase, participants exhibited disrupted sleep cycles, characterized by prolonged periods of continuous activity between 10 p.m. and 2 a.m., indicating a blurring of day-night activity boundaries. As the clinical diagnosis approached, researchers observed a marked decline in evening activity between 6 p.m. and 10 p.m., accompanied by extended inactive periods. Environmental monitoring further highlighted that sustained low indoor humidity correlated strongly with elevated diagnostic risk, suggesting that household climate control may serve as an auxiliary health indicator. The development addresses a critical gap in geriatric healthcare, where elderly patients often struggle to articulate early symptoms or recall subtle physiological changes. By automating the observation of routine domestic habits, the technology provides caregivers and medical professionals with objective, longitudinal health metrics. The research team emphasizes that the system is not intended to replace diagnostic procedures or forecast exact disease onset, but rather to function as an early warning mechanism that facilitates timely medical consultation. Lead investigator Professor Lisa Lim of KAIST stressed the preventive utility of the platform, noting that early detection of micro-pattern changes in daily life can bridge the critical window between symptom onset and clinical intervention. The team plans to conduct prospective validations across larger demographic cohorts to support eventual clinical deployment. If successful, this AI-driven domestic monitoring framework could fundamentally restructure elderly care models, prioritizing continuous environmental and behavioral analysis to prevent catastrophic cerebrovascular events before they manifest.