Worker Monitor

An advanced ESP32-based worker condition monitoring system that collects biometric and environmental data through multiple sensors to ensure worker safety in real-time.

Comprehensive Monitoring System

Real-time data collection from multiple sensor types for complete worker safety oversight

Motion Sensing

Advanced 9-axis motion detection with fall detection capabilities

  • MPU6500 accelerometer, gyroscope & magnetometer
  • Real-time orientation tracking
  • Fall detection algorithms
  • Rockfall detection system

Environmental Monitoring

Complete environmental condition tracking for worker safety

  • BMP280 pressure & temperature sensor
  • DHT22 humidity & temperature sensor
  • Altitude calculations
  • Heat index monitoring

Biometric Tracking

Real-time health monitoring with advanced pulse oximetry

  • MAX30102 pulse oximeter & heart rate sensor
  • Real-time heart rate monitoring
  • Blood oxygen level detection
  • Finger detection algorithms

Web Dashboard

Modern web interface with real-time data visualization

  • WebSocket real-time communication
  • 3D orientation visualization
  • SQLite data storage
  • Responsive design

Technology Stack

Built with modern technologies and industry-standard components

Python Backend

Asyncio WebSocket server with aiohttp

ESP32

Dual-core microcontroller with WiFi

SQLite

Lightweight database for sensor data

Chart.js

Interactive data visualization

Three.js

3D orientation visualization

WebSockets

Real-time bidirectional communication

Quick Setup Guide

Get your worker monitoring system up and running in minutes

1

Hardware Setup

Connect your sensors to the ESP32 development board:

  • MPU6500, BMP280, MAX30102: SDA→GPIO21, SCL→GPIO22
  • DHT sensor: Data pin→GPIO16
  • Buzzer: VCC→D14
2

Clone Repository

Download the project and install dependencies:

git clone https://github.com/he1senbrg/worker-monitor.git
3

Configure & Flash

Update WiFi credentials and flash the ESP32 using PlatformIO IDE. Configure server connection settings as needed.

4

Start Monitoring

Run the Python server and access the web dashboard to view real-time sensor data and worker condition monitoring.