At-home rehabilitation for post-stroke patients presents significant challenges, as continuous, personalized care is often limited outside clinical settings. Additionally, the absence of comprehensive solutions addressing diverse rehabilitation needs in home environments complicates recovery efforts. Here, we introduce a smart home platform that integrates wearable sensors, ambient monitoring, and large language model (LLM)-powered assistance to provide seamless health monitoring and intelligent support. The system leverages machine learning enabled plantar pressure arrays for motor recovery assessment (94% classification accuracy), a wearable eye-tracking module for cognitive evaluation, and ambient sensors for precise smart home control (100% operational success, <1 s latency). Additionally, the LLM-powered agent, Auto-Care, offers real-time interventions, such as health reminders and environmental adjustments, enhancing user satisfaction by 29%. This work establishes a fully integrated platform for long-term, personalized rehabilitation, offering new possibilities for managing chronic conditions and supporting aging populations.

翻译：脑卒中患者的家庭康复面临重大挑战，临床环境之外往往难以获得持续、个性化的护理。此外，家庭环境中缺乏能够满足多样化康复需求的综合解决方案，这进一步增加了康复的复杂性。本文提出一种智能家居平台，该平台集成了可穿戴传感器、环境监测以及基于大语言模型（LLM）的辅助功能，以提供无缝的健康监测与智能支持。系统利用基于机器学习的足底压力阵列进行运动功能恢复评估（分类准确率达94%），采用可穿戴眼动追踪模块进行认知评估，并利用环境传感器实现精确的智能家居控制（操作成功率100%，延迟<1秒）。此外，基于LLM的智能体Auto-Care可提供实时干预，如健康提醒与环境调节，将用户满意度提升了29%。本研究构建了一个完全集成的、适用于长期个性化康复的平台，为慢性病管理与老龄化人口支持提供了新的可能性。