# 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.

## Prerequisites

Ensure your environment meets the [System Requirements](./get-started/system-requirements.md) before proceeding.

## 1. Clone the Repository

Go to the target directory of your choice and clone the suite.
<!--If you want to clone a specific release branch, replace `release-2026.1.0` with the desired tag.-->
To learn more on partial cloning, check the [Repository Cloning guide](https://docs.openedgeplatform.intel.com/2026.1/OEP-articles/contribution-guide.html#repository-cloning-partial-cloning).

```bash
git clone --filter=blob:none --sparse --branch release-2026.1.0 https://github.com/open-edge-platform/edge-ai-suites.git
cd edge-ai-suites
git sparse-checkout set health-and-life-sciences-ai-suite
cd health-and-life-sciences-ai-suite/multi_modal_patient_monitoring
```

## 2. 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:

1. Open `configs/device.env` in a text editor.
2. Locate the entries for `ECG_DEVICE`, `RPPG_DEVICE`, `MDPNP_DEVICE`, and `POSE_3D_DEVICE`.
3. 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.

## 3. Run the Sample

### Run Using Pre‑Built Images (Registry Mode)

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

```bash
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:

```bash
# 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 stop and remove all containers when you are done:

```bash
make down
```

## 4. 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.

## 5. 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.

## 6. 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](./how-it-works.md) 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.


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get-started/system-requirements.md

:::