HiMe让可穿戴设备实时生成个人健康洞察，隐私安全且本地部署。
HiMe: Real-Time Self-Hosted Personal Agent Platform for Health Insights with Wearable Devices
- 构建本地运行的健康代理平台，支持多设备实时数据处理。
- 通过长期用户建模与实时分析结合，实现个性化健康监测。
- 适合关注隐私、需持续健康管理的个人用户使用。
传统可穿戴健康信号分析方法受限于僵化的分析框架和个性化不足。大模型代理的出现为个人健康智能分析带来新可能，使健康洞察能自适应且上下文感知地生成。然而，目前尚无开源的本地部署平台能实现实时处理个人健康数据并保障隐私。本文提出HiMe，一个完全兼容多种可穿戴设备、支持实时健康数据生态的本地部署、隐私优先的代理平台。其设计遵循三项原则：将数据库作为核心组件；在效果与效率间协同优化，实现低成本帕累托最优平衡；实时处理数据的同时长期建模用户。这些原则使个人能够持续、个性化地利用健康代理提升健康水平。
原文摘要 · Abstract (English)
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.
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