Multi-Modal Patient Monitoring

GitHub Readme

Note! This application is for reference and evaluation purposes. It is not intended for direct use in clinical or diagnostic environments and is not validated as such.

The Multi-Modal Patient Monitoring application is a reference solution that demonstrates how multiple AI pipelines can run simultaneously on a single Intel® platform, providing workflows that mimic a consolidated patient monitoring application.

It combines several representative AI workloads and 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 (Medical Device Plug-and-Play): 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 – Step-by-step instructions to build and run the application using make and Docker.

• System Requirements – Hardware, software, and network requirements, plus an overview of the AI models used by each workload.

• How It Works – High-level architecture, service responsibilities, and data/control flows.

• Release Notes – Version history and known issues.