发表机构
King’s College London; The Alan Turing Institute（伦敦国王学院; 艾伦·图灵研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对传统可穿戴健康信号分析的局限，HiMe提出本地可部署、隐私至上的代理平台，遵循数据库为一等组件等原则，能与多种可穿戴设备实时健康数据生态系统兼容，实现个人持续、个性化健康监测以提升幸福感。
AI 中文摘要
传统可穿戴健康信号分析方法，如智能手表，受限于僵化分析框架和有限个性化。大语言模型代理的出现为个人健康代理分析带来新机遇，可自适应且结合情境生成健康洞察。但目前尚无能实时处理个人健康数据并保护隐私的开源本地可部署平台。我们提出HiMe，一个本地可部署、隐私至上的代理平台，与多种可穿戴设备的实时健康数据生态系统完全兼容。HiMe遵循三个设计原则：将数据库视为一等组件；联合优化有效性和效率以实现低成本帕累托最优平衡；在长期对用户建模的同时实时处理数据。这些原则使个人利用个人健康代理进行持续、个性化健康监测以提升幸福感成为现实。
英文摘要
Traditional approaches to wearable health signal analysis, such as smartwatches, are constrained by rigid analytical frameworks and limited personalisation. The emergence of LLM agents creates a new opportunity for Personal Health Agentic Analysis, where health insights can be generated adaptively and in context. However, currently there is no open-source locally deployable platform capable of processing personal health data in real time while preserving privacy. We present HiMe, a locally deployable, privacy-first agent platform that is fully compatible with real-time health data ecosystems across a wide range of wearable devices. HiMe is guided by three design principles. The database is treated as a first-class component. Effectiveness and efficiency are jointly optimised to achieve a low-cost Pareto-optimal balance. Data are processed in real time while the user is modelled over the long term. Together, these principles make it practical for individuals to harness Personal Health Agents for continuous, personalised health monitoring for better wellbeing.
