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arXiv · 2609.22024

Beyond Reactive Assistance: PV-Care Using Low-Density EEG and AI to Provide Proactive, Context-Aware Help for MCI

Simon L Liu·Manish Kumar Krishne Gowda
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Abstract

The growing elderly population gives rise to an urgent need for intelligent support systems, particularly for individuals with Mild Cognitive Impairment (MCI). This paper presents PV-Care, a proactive AI-driven assistance scheme that integrates wearable electroencephalogram (EEG) sensing with visual environmental perception to provide real-time, context-aware voice assistance for MCI users. Unlike traditional assistant systems that passively wait for user commands, PV-Care actively initiates helpful interactions based on the user's detected brain states, including Learning, Memory Recall, and Resting, using a novel deep neural architecture named Spatial and Frequency Refinement Network (SFR-Net). By combining EEG-based cognitive-state recognition with AI-based visual analysis, PV-Care generates structured "4W-UT" prompts to guide the output of large language models (LLMs). Simulation results and user studies validate the high accuracy of the proposed SFR-Net and the effectiveness of PV-Care's context-aware assistance. These results indicate that PV-Care is a feasible and promising solution for MCI caring.

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Simon L Liu, Manish Kumar Krishne Gowda. 2026-09-18. Beyond Reactive Assistance: PV-Care Using Low-Density EEG and AI to Provide Proactive, Context-Aware Help for MCI. https://arxiv.org/abs/2609.22024

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