Smart-Health-Tracking-App

Introduction

The convergence of IoT sensors and advanced health monitoring technologies, such as Photoplethysmography (PPG), has ushered in a new era of remote health assessment and fitness tracking. PPG-based IoT sensors provide real-time insights into various health metrics, including heart rate, blood oxygen levels, sleep patterns, and stress levels. This project presents a novel solution featuring:

1. An IoT-ML enabled platform for self-monitoring of important health vitals.
2. A fitness assessment score derived through a machine learning (ML) algorithm using the health vitals of a person.

By integrating data on various health parameters, including heart rate, blood oxygen levels, and sleep patterns, advanced machine learning models analyze this data to predict a fitness score that accurately reflects an individual’s overall health and fitness level.

Features

• Unified monitoring of major health vitals.
• Fitness assessment score derived from health vitals.
• Integration of IoT hardware for real-time health monitoring.

App Interface

Architecture

The IoT hardware collects real-time health vitals, which are processed by the ML models. The ML models, developed in Python, analyze these vitals to compute a fitness score. This score and the vitals data are then displayed in the Android application, providing users with a comprehensive view of their health.

How to Use

1. Setup IoT Hardware: Connect the sensors (MAX30100, DS18B20, MPU6050) to the Arduino UNO.
2. Run ML Models: Use the provided Python scripts to run the ML models and compute the fitness score.
3. Deploy Android App: Open the project in Android Studio, integrate with Firebase, and deploy the app to your device.

Technologies Used

Machine Learning

Developed using Python, the following ML models were utilized:

• Linear Regression
• DecisionTreeRegressor
• XGBoost Regressor

IoT Hardware

• MAX30100 (Pulse Oximeter and Heart-Rate Sensor)
• DS18B20 (Temperature Sensor)
• MPU6050 (Gyroscope and Accelerometer)
• Arduino UNO

Mobile Application

• Android Studio (Java)
• Firebase (Backend as a Service)
• ML Models integrated via pickling

Getting Started

Prerequisites

• Python 3.x
• Arduino IDE
• Android Studio
• Firebase Account

Installation

1. Clone the Repository:

   git clone https://github.com/yourusername/health-monitoring-app.git
   cd health-monitoring-app