# Templates / Heartrate Documentation – TypeScript

## Included Files
1. templates/heartrate/heartrate.mdx

## Live Heart Rate Monitoring Template

Source: templates/heartrate/heartrate.mdx

Build a real-time health analytics dashboard with Moose, Streamlit, and Python

# Live Heart Rate Leaderboard Template

## Overview

The Live Heart Rate Leaderboard template demonstrates how to build a real-time health monitoring application using Moose. It features a Streamlit-based dashboard that displays live heart rate data, calculates performance metrics, and maintains a competitive leaderboard.

## Features

- Real-time heart rate monitoring dashboard with interactive graphs
- Live leaderboard tracking multiple users
- Heart rate zone visualization
- Performance metrics calculation (power output and calories burned)
- User-specific data tracking and visualization

## Architecture

### Moose Data Pipeline Backend

The template implements a three-stage data processing pipeline:

1. **Raw Data Ingestion** (`RawAntHRPacket`)
   - Captures raw heart rate data from ANT+ devices
   - Includes basic device and timestamp information

2. **Data Processing** (`ProcessedAntHRPacket`)
   - Transforms raw data into processed format
   - Adds calculated metrics and validation

3. **Unified Format** (`UnifiedHRPacket`)
   - Standardizes heart rate data for analytics
   - Includes user information and derived metrics

### Streamlit Frontend Dashboard

The Streamlit dashboard (`streamlit_app.py`) provides:

- Real-time heart rate visualization
- Performance metrics display
- Interactive user selection
- Live-updating leaderboard
- Heart rate zone indicators

## Getting Started

Python 3.12+
Moose CLI

```bash
bash -i <(curl -fsSL https://fiveonefour.com/install.sh) moose
```

1. Create a new Moose project from the template:
```bash
moose init moose-heartrate live-heartrate-leaderboard
cd moose-heartrate
```
2. Create a new virtual environment and install the dependencies:
```bash
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
```
3. Configure your environment variables in `.env`

4. Start the Moose pipeline:
```bash
moose dev
```

5. Launch the Streamlit dashboard:
```bash
streamlit run streamlit_app.py
```

## API Endpoints

The template exposes two main API endpoints:

- `/api/getUserLiveHeartRateStats`
  - Parameters: user_name, window_seconds
  - Returns: Recent heart rate statistics for a specific user

- `/api/getLeaderboard`
  - Parameters: time_window_seconds, limit
  - Returns: Ranked list of users based on performance metrics

## Data Models

### UnifiedHRPacket
```python
from moose_lib import Key
from pydantic import BaseModel
from datetime import datetime

class UnifiedHRPacket(BaseModel):
    user_id: Key[int]
    user_name: str
    device_id: int
    hr_timestamp_seconds: float
    hr_value: float
    rr_interval_ms: float
    processed_timestamp: datetime
```

## Performance Calculations

The template includes calculations for:
- Heart rate zones (1-5)
- Estimated power output
- Cumulative calories burned
- Average performance metrics

## Customization

You can customize the template by:
- Modifying heart rate zone thresholds
- Adjusting performance calculation formulas
- Extending the data pipeline with additional metrics
- Customizing the dashboard layout and visualizations