At-home rehabilitation for post-stroke patients presents significant challenges, as continuous, personalized care is often limited outside clinical settings. Moreover, the lack of integrated solutions capable of simultaneously monitoring motor recovery and providing intelligent assistance in home environments hampers rehabilitation outcomes. Here, we present a multimodal smart home platform designed for continuous, at-home rehabilitation of post-stroke patients, integrating wearable sensing, ambient monitoring, and adaptive automation. A plantar pressure insole equipped with a machine learning pipeline classifies users into motor recovery stages with up to 94\% accuracy, enabling quantitative tracking of walking patterns during daily activities. An optional head-mounted eye-tracking module, together with ambient sensors such as cameras and microphones, supports seamless hands-free control of household devices with a 100\% success rate and sub-second response time. These data streams are fused locally via a hierarchical Internet of Things (IoT) architecture, ensuring low latency and data privacy. An embedded large language model (LLM) agent, Auto-Care, continuously interprets multimodal data to provide real-time interventions -- issuing personalized reminders, adjusting environmental conditions, and notifying caregivers. Implemented in a post-stroke context, this integrated smart home platform increased mean user satisfaction from 3.9 $\pm$ 0.8 in conventional home environments to 8.4 $\pm$ 0.6 with the full system ($n=20$). Beyond stroke, the system offers a scalable, patient-centered framework with potential for long-term use in broader neurorehabilitation and aging-in-place applications.

翻译：脑卒中患者的居家康复面临重大挑战，因为临床环境之外通常难以获得持续、个性化的护理。此外，缺乏能够在家庭环境中同时监测运动恢复情况并提供智能辅助的集成解决方案，阻碍了康复效果。本文提出一种专为脑卒中患者持续居家康复设计的多模态智能家居平台，该平台集成了可穿戴传感、环境监测和自适应自动化技术。一个配备机器学习流程的足底压力鞋垫能够以高达94%的准确率将用户分类至不同的运动恢复阶段，从而实现对日常活动中行走模式的定量追踪。一个可选的头戴式眼动追踪模块，与摄像头、麦克风等环境传感器协同工作，支持对家居设备进行无缝、免提控制，成功率达100%，响应时间低于1秒。这些数据流通过分层物联网架构在本地进行融合，确保了低延迟和数据隐私。一个嵌入式大语言模型智能体——Auto-Care——持续解读多模态数据以提供实时干预，包括发出个性化提醒、调整环境条件以及通知护理人员。在脑卒中后康复场景中的实施结果表明，该集成智能家居平台将用户平均满意度从常规家居环境下的3.9 $\pm$ 0.8提升至完整系统下的8.4 $\pm$ 0.6（$n=20$）。除脑卒中康复外，该系统提供了一个可扩展的、以患者为中心的框架，具有在更广泛的神经康复和居家养老应用中长期使用的潜力。