HiMe: Real-Time Self-Hosted Personal Agent Platform for Health Insights with Wearable Devices

2026-07-23

AI Summary

This work addresses critical limitations in existing wearable health data analysis frameworks—namely, rigidity, insufficient personalization, inadequate privacy safeguards, and the absence of localized platforms for real-time processing. To overcome these challenges, we propose and implement a privacy-first, locally deployed personal health agent system that uniquely treats the database as a first-class component. By integrating large language model agents, real-time stream processing, and long-term user modeling, our approach constructs a Pareto-optimal, lightweight analytical architecture. The resulting platform supports heterogeneous wearable devices and enables low-overhead, real-time responsiveness alongside continuous personalized health insights—all while preserving data privacy and ensuring practical deployability for intelligent health monitoring.

Abstract

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.

Problem

• wearable devices
• personal health analysis
• real-time processing
• privacy preservation
• personalisation

Innovation

• Personal Health Agent
• Real-Time Processing
• Privacy-Preserving
• Wearable Integration
• Local Deployment