Open Claw Edge Secure Health

Autonomous on‑device AI for early cardiac risk detection in Richmond hospitals and community systems, secured by Intel® TME.

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Modern healthcare digitalization faces a critical dilemma: how to monitor vital signs in real-time without exposing sensitive data to the cloud or suffering from data injection attacks? Inspired by the need for clinics in areas with unstable connectivity and the strict European privacy standards (GDPR in Vienna), I created HealthClaw. My goal was to merge the autonomy of AI agents with the physical security of dedicated hardware. ⚙️ How I Built It The project was built on three core technological pillars:

OpenClaw Orchestration: I utilized the OpenClaw framework to create the Sentinel Agent, an autonomous agent that monitors the environment, detects Intel hardware, and manages data workflows.
Edge AI with Intel® OpenVINO™: I integrated my proprietary models, CardioNetV2_SecureEngine.exe and cardionetrpm.exe, optimized for ultra-low latency execution (
) directly on the CPU.
Hardware Shielding: I implemented a verification logic for Intel® TME (Total Memory Encryption) and SGX, ensuring that clinical inference occurs within a "secure enclave" at the silicon level.

🧠 Challenges I Faced The biggest challenge was ensuring the OpenClaw agent could "see" and validate hardware components and proprietary binary files independently, without relying on cloud APIs. I developed a robust Python wrapper that performs local scans of IPs, Bluetooth (BLE) status, and file integrity, allowing the system to operate 100% Offline. 🎓 What I Learned I learned that the future of AI in healthcare is not just about accurate models, but about Agentic AI. By transforming a static model into an agent orchestrated by OpenClaw, the system moves from simply "measuring" to "protecting"—becoming capable of dropping malicious data packets instantly upon detecting digital signature failures. 🚀 What's Next for HealthClaw The next step is to expand the agent library to include NeuroNetV2, allowing for simultaneous brain and heart monitoring, all orchestrated locally and protected by total memory encryption. by implement the firsr Local I.A for predict cardiac risks with the hospital computers already have. FOR IP REASONS THE MODELO XML AND BIN ARE PROPRIETARY - Full model access is available for the judging panel upon request via a private repository under NDA - OR PRIVATE REPOSITORY IF NEEDED. EdgeHealthCare AI is a location-agnostic infrastructure, and we are actively seeking strategic partnerships in Richmond, VA, to launch a dedicated pilot project. Our goal is to collaborate with local senior care facilities and neurodiversity centers to validate our predictive engine in a real-world civic environment. We are fully equipped to receive international funding and handle cross-border R&D operations, ensuring that this investment directly translates into scalable safety for Richmond’s citizens

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