GitHub project Readme

# Multi-Modal Patient Monitoring The Multi-Modal Patient Monitoring application is a reference workload that demonstrates how multiple AI pipelines can run simultaneously on a single Intel® platform, providing consolidated monitoring for a virtual patient. It combines several AI services: - **rPPG (Remote Photoplethysmography):** Contactless heart and respiratory rate estimation from facial video. - **3D-Pose Estimation:** 3D human pose detection from video. - **AI-ECG:** ECG rhythm classification from simulated ECG waveforms. - **MDPNP:** Getting metrics of three simulated devices such as ECG, BP and CO2 - **Patient Monitoring Aggregator:** Central service that collects and aggregates vitals from all AI workloads. - **Metrics Collector:** Gathers hardware and system telemetry (CPU, GPU, NPU, power) from the host. - **UI:** Web-based dashboard for visualizing waveforms, numeric vitals, and system status. Together, these components illustrate how vision- and signal-based AI workloads can be orchestrated, monitored, and visualized in a clinical-style scenario. ## Supporting Resources - [Get Started](./get-started.md) – Step-by-step instructions to build and run the application using `make` and Docker. - [System Requirements](./get-started/system-requirements.md) – Hardware, software, and network requirements, plus an overview of the AI models used by each workload. - [How It Works](./how-it-works.md) – High-level architecture, service responsibilities, and data/control flows. > This application is provided for development and evaluation purposes only and is *not* intended for clinical or diagnostic use. :::{toctree} :hidden: get-started.md how-it-works.md :::