[DOCS] HLS - review

health-and-life-sciences-ai-suite/multi_modal_patient_monitoring/docs/user-guide/get-started.md

@@ -7,7 +7,7 @@ GPU, or NPU.
 Before you begin, review the [System Requirements](./get-started/system-requirements.md) to
 ensure your environment meets the recommended hardware and software prerequisites.
 
-## Clone the Repository
+## 1. Clone the Repository
 
 > **Note:** Make sure you are in the `multi_modal_patient_monitoring` directory before running
 > the commands in this guide.
@@ -28,7 +28,7 @@ git checkout main
 cd health-and-life-sciences-ai-suite/multi_modal_patient_monitoring
 ```
 
-## Configure Hardware Target
+## 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`:
@@ -50,7 +50,9 @@ When you run `make run` or `make run REGISTRY=false`, the compose file reads
 inference engine compiles its OpenVINO model on the requested device, with automatic fallback
 to CPU when necessary.
 
-## Run Using Pre‑Built Images (Registry Mode)
+## 3. Run the Sample
+
+### Run Using Pre‑Built Images (Registry Mode)
 
 If you want to use pre‑built images from a container registry, run:
 
@@ -64,7 +66,7 @@ This will:
 - 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
+### 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:
@@ -80,13 +82,13 @@ 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:
+To stop and remove all containers when you are done:
 
 ```bash
 make down
 ```
 
-## Access the UI
+## 4. Access the UI
 
 By default, the UI service exposes port 3000 on the host:
 
@@ -95,7 +97,7 @@ By default, the UI service exposes port 3000 on the host:
 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
+## 5. Control RPPG Streaming
 
 The rPPG service provides a simple HTTP control API (hosted by an internal FastAPI server) to
 start and stop streaming:
@@ -108,7 +110,7 @@ start and stop streaming:
 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
+## 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:

health-and-life-sciences-ai-suite/multi_modal_patient_monitoring/docs/user-guide/get-started/system-requirements.md

@@ -50,7 +50,7 @@ This section lists the hardware, software, and network requirements for running
 
 ## AI Models and Workloads
 
-The application bundles several AI workloads, each with its own model(s) and inputs/outputs:
+The application bundles several AI workloads, each with its own model and inputs or outputs:
 
 - **RPPG (Remote Photoplethysmography) Workload:**
   - **Model:** MTTS‑CAN (Multi‑Task Temporal Shift Convolutional Attention Network)

health-and-life-sciences-ai-suite/multi_modal_patient_monitoring/docs/user-guide/how-it-works.md

@@ -78,7 +78,7 @@ observability of AI workloads:
   can expose summarized metrics via its own API.
 
 These metrics are useful for validating that AI workloads are correctly utilizing Intel
-accelerators and for performance benchmarking.
+accelerators, and for performance benchmarking.
 
 ### UI Service
 

health-and-life-sciences-ai-suite/multi_modal_patient_monitoring/docs/user-guide/index.md

@@ -11,7 +11,8 @@ hide_directive-->
 
 # 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
+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:
@@ -20,14 +21,16 @@ It combines several AI services:
   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
+- **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.
+Together, these components illustrate how vision- and signal-based AI workloads can be
+orchestrated, monitored, and visualized in a clinical-style scenario.
 
 ## Supporting Resources
 
@@ -45,7 +48,6 @@ Together, these components illustrate how vision- and signal-based AI workloads
 
 get-started.md
 how-it-works.md
-run-multi-modal-app.md
 release-notes.md
 
 :::