超越被动辅助：PV-Care 利用低密度脑电图和人工智能为轻度认知障碍提供主动、情境感知的帮助
Beyond Reactive Assistance: PV-Care Using Low-Density EEG and AI to Provide Proactive, Context-Aware Help for MCI
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中文总结 AI 辅助
本文提出PV-Care，一种结合低密度脑电图与视觉感知的主动AI辅助系统，通过SFR-Net识别认知状态并生成4W-UT提示，为轻度认知障碍用户提供实时情境感知语音帮助。
中文摘要 AI 辅助
日益增长的老年人口引发了对智能支持系统的迫切需求，特别是针对患有轻度认知障碍（MCI）的个体。本文提出了PV-Care，一种主动的AI驱动辅助方案，该方案将可穿戴脑电图（EEG）传感与视觉环境感知相结合，为MCI用户提供实时的、情境感知的语音辅助。与被动等待用户命令的传统辅助系统不同，PV-Care基于检测到的用户大脑状态（包括学习、记忆回忆和静息）主动发起有益的交互，使用一种名为空间与频率细化网络（SFR-Net）的新型深度神经架构。通过将基于EEG的认知状态识别与基于AI的视觉分析相结合，PV-Care生成结构化的“4W-UT”提示来指导大型语言模型（LLMs）的输出。仿真结果和用户研究验证了所提出的SFR-Net的高准确性以及PV-Care情境感知辅助的有效性。这些结果表明，PV-Care是MCI护理中一种可行且有前景的解决方案。
英文摘要
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.
发表机构
- Shanghai High School International Division（上海中学国际部）
- Elmore Family School of Electrical and Computer Engineering, Purdue University（普渡大学埃尔莫电气与计算机工程学院）
- Apple Inc.（苹果公司）
机构由 AI 辅助整理，请以论文原文为准。
