Smart Wearable Device - Pulse Pro

Pulse Pro is an IoT project for health monitoring. It features a hardware device that measures SpO2, temperature, and heart rate, with data displayed and analyzed in the Pulse Pro app, offering users insights and personalized health recommendations.

Architecture

Features

Pulse Pro Client App:

Work Flow:

The Pulse Pro client app uses React for the frontend and Firebase for the backend to provide a robust health monitoring system. After user authentication via Firebase, the app displays real-time pulse rate and oxygen levels, fetched from an ESP8266 microcontroller, on a dynamic dashboard. The app sends this data to a Flask server, where the Gemini model generates actionable health insights, helping users better understand their health metrics.

Real-Time Health Monitoring:

Instantly view measurements of SpO2, temperature, and heart rate.

Data Analysis:

Track and analyze health data trends over time to gain insights into your overall well-being.

Personalized Recommendations:

Receive tailored food recommendations based on your heart and pulse rate.

Health Tracking:

Continuously monitor vital signs to stay informed about your health status.

Pulse Pro Care Provider App:

Work Flow:

HCPs can add users to the Pulse Pro system and access their information using Firestore Database, while Firebase Realtime Database delivers real-time health updates. This setup ensures that HCPs can monitor their clients' health metrics efficiently and respond promptly to any changes.

Client Health Monitoring:

Healthcare providers (HCPs) can access and monitor the health data of their clients.

Comprehensive Dashboard:

View all client data in one place, enabling efficient health management.

SOS Alerts:

Immediate notifications for HCPs when a client's health metrics indicate potential risks.

Recommendation System:

Provide clients with diet and exercise recommendations to support their health goals.

Hardware Specifications

Data Enhancement

Compression:

The autoencoder takes your data and compresses it into a smaller, dense format. This is done by the encoder part of the network.

Reconstruction:

It then tries to reconstruct the original data from this compressed format using the decoder part of the network.

Anomaly Detection:

If the autoencoder struggles to reconstruct certain data accurately (high reconstruction error), it’s likely that the data is different from what it has learned as normal. This can be used to identify anomalies or outliers.

SpO2 Sensor:

Measures blood oxygen levels.

Temperature Sensor:

Monitors body temperature.

Heart Rate Sensor:

Tracks heart rate in real-time.