迈向人-机-机器人协作与基于AI智能体的数字孪生用于帕金森病管理：综述与展望
Towards Human-AI-Robot Collaboration and AI-Agent based Digital Twins for Parkinson's Disease Management: Review and Outlook
AI总结：
本文综述了帕金森病多模态感知与机器人康复两条独立研究路线，提出以AI智能体和大语言模型为核心的闭环传感器-AI-机器人框架，并展望构建可自适应演化的PD患者数字孪生以实现个性化智能护理。
摘要：
当前关于帕金森病（PD）筛查、监测和管理的研究沿着两条基本独立的轨迹发展。第一个研究群体专注于使用非侵入性技术对PD相关生物标志物进行多模态感知，这些技术包括惯性测量单元（IMUs）、力/压力鞋垫、肌电图（EMG）、脑电图（EEG）、语音和声学分析以及RGB/RGB-D运动捕捉系统。这些研究强调数据采集、特征提取和基于机器学习的分类，用于PD筛查、诊断和疾病进展建模。与此同时，第二个研究群体专注于机器人干预和康复，采用社交辅助机器人（SARs）、机器人辅助康复（RAR）系统以及虚拟现实（VR）集成的机器人平台，以改善运动和认知功能、增强社会参与并支持照护者。尽管这两个领域的目标互补，但它们的方法论和技术整合仍然有限，两者之间在数据层面或决策层面的耦合极少。随着先进人工智能（AI）的出现，包括大语言模型（LLMs）和智能体AI系统，现在出现了一个独特的机会来统一这些研究方向。我们设想一个闭环的传感器-AI-机器人框架，其中多模态感知通过AI智能体持续引导患者、照护者、人形机器人（和医生）之间的交互，这些AI智能体由多种AI模型驱动，例如机器人和可穿戴基础模型、基于LLM的推理、强化学习和持续学习。这种闭环系统能够实现个性化、可解释和情境感知的干预，为PD患者的数字孪生奠定基础，该数字孪生可以随时间自适应，以提供智能的、以患者为中心的PD护理。
英文摘要：
The current body of research on Parkinson's disease (PD) screening, monitoring, and management has evolved along two largely independent trajectories. The first research community focuses on multimodal sensing of PD-related biomarkers using noninvasive technologies such as inertial measurement units (IMUs), force/pressure insoles, electromyography (EMG), electroencephalography (EEG), speech and acoustic analysis, and RGB/RGB-D motion capture systems. These studies emphasize data acquisition, feature extraction, and machine learning-based classification for PD screening, diagnosis, and disease progression modeling. In parallel, a second research community has concentrated on robotic intervention and rehabilitation, employing socially assistive robots (SARs), robot-assisted rehabilitation (RAR) systems, and virtual reality (VR)-integrated robotic platforms for improving motor and cognitive function, enhancing social engagement, and supporting caregivers. Despite the complementary goals of these two domains, their methodological and technological integration remains limited, with minimal data-level or decision-level coupling between the two. With the advent of advanced artificial intelligence (AI), including large language models (LLMs), agentic AI systems, a unique opportunity now exists to unify these research streams. We envision a closed-loop sensor-AI-robot framework in which multimodal sensing continuously guides the interaction between the patient, caregiver, humanoid robot (and physician) through AI agents that are powered by a multitude of AI models such as robotic and wearables foundation models, LLM-based reasoning, reinforcement learning, and continual learning. Such closed-loop system enables personalized, explainable, and context-aware intervention, forming the basis for digital twin of the PD patient that can adapt over time to deliver intelligent, patient-centered PD care.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
