Get Started#

This guide walks you through building and running the multi‑modal patient monitoring reference application, including the rPPG (remote photoplethysmography) service running on Intel CPU, GPU, or NPU.

Before you begin, review the System Requirements to ensure your environment meets the recommended hardware and software prerequisites.

Clone the Repository#

Note: Make sure you are in the multi_modal_patient_monitoring directory before running the commands in this guide.

If you have not already cloned the repository that contains this workload, do so now:

git clone <your-hl-ai-suite-repo-url>
cd edge-ai-suites/health-and-life-sciences-ai-suite/multi_modal_patient_monitoring

Configure Hardware Target#

Each AI workload uses a device environment variable to select its OpenVINO target device. These are defined in configs/device.env:

ECG_DEVICE – device for the AI‑ECG workload (for example, GPU).
RPPG_DEVICE – device for the rPPG workload (CPU, GPU, or NPU).
MDPNP_DEVICE – device for MDPnP processing (for example, CPU).
POSE_3D_DEVICE – device for the 3D‑pose estimation workload (for example, GPU).

To configure these:

Open configs/device.env in a text editor.
Locate the entries for ECG_DEVICE, RPPG_DEVICE, MDPNP_DEVICE, and POSE_3D_DEVICE.
Set each to the appropriate device string supported on your system (typically CPU or GPU, and NPU where available and supported).

When you run make run or make run REGISTRY=false, the compose file reads configs/device.env and passes these values into the corresponding services so that each inference engine compiles its OpenVINO model on the requested device, with automatic fallback to CPU when necessary.

Run Using Pre‑Built Images (Registry Mode)#

If you want to use pre‑built images from a container registry, run:

make run

This will:

Pull the required images from the configured registry.
Start all services defined in docker-compose.yaml in detached mode.
Print the URL of the UI (for example, http://<HOST_IP>:3000).

Run Using Locally Built Images#

If you prefer to build the images locally instead of pulling from a registry, run the following commands from the multi_modal_patient_monitoring directory:

# Initialize MDPnP submodule
make init-mdpnp

# Build and run all containers locally (no registry pulls)
make run REGISTRY=false

The Makefile wraps the underlying docker compose commands and ensures that all dependent components (MDPnP, DDS bridge, AI services, and UI) are started with the correct configuration.

To tear everything down when you are done:

make down

Access the UI#

By default, the UI service exposes port 3000 on the host:

Open a browser and go to: http://localhost:3000

From there you can observe heart rate and respiratory rate estimates, along with waveforms produced by the rPPG service and aggregated by the patient‑monitoring‑aggregator.

Control RPPG Streaming#

The rPPG service provides a simple HTTP control API (hosted by an internal FastAPI server) to start and stop streaming:

Start streaming:
- Send a request to the /start endpoint on the rPPG control port (default 8084).
Stop streaming:
- Send a request to the /stop endpoint on the same port.

Exact URLs and endpoints may differ slightly depending on how the control API is exposed in your environment; refer to the rPPG service documentation for details.

View Hardware Metrics#

The metrics-collector service writes telemetry (GPU, NPU, CPU, power, and other metrics) into the metrics directory on the host, and may also expose summarized metrics via its own API:

Inspect raw logs under the metrics directory mounted in the compose file.
Combine these metrics with the rPPG output and UI dashboards to evaluate accelerator utilization and end‑to‑end performance.

Next Steps#

Learn more about How It Works for a high-level architectural overview.
Experiment with different RPPG_DEVICE values to compare CPU, GPU, and NPU behavior.
Replace the sample video or models with your own assets by updating the models and videos volumes and configuration.