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 consumption
   • 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

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

1. Create a new Moose project from the template:

moose init moose-heartrate live-heartrate-leaderboard
cd moose-heartrate

2. Create a new virtual environment and install the dependencies:

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:

moose dev

5. Launch the Streamlit dashboard:

streamlit run app/streamlit_app.py

API Endpoints

The template exposes two main API endpoints:

• /consumption/getUserLiveHeartRateStats

  • Parameters: user_name, window_seconds
  • Returns: Recent heart rate statistics for a specific user

• /consumption/getLeaderboard

  • Parameters: time_window_seconds, limit
  • Returns: Ranked list of users based on performance metrics

Data Models

UnifiedHRPacket

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