Wrist Pulse Sensing System with ESP32 and Max30100 Sensors

Project Overview

The Wrist Pulse Sensing System is a health monitoring project designed to sense and analyze Vatta, Pitha, and Kapha waveforms using an ESP32 microcontroller, a multiplexer (MUX), and three Max30100 pulse oximeter sensors.

Table of Contents

• Components Used
• System Operation
• Benefits
• Future Developments
• Getting Started
• Usage
• Contributing
• License

Components Used

• ESP32 Microcontroller: The brain of the system, responsible for data processing and communication.
• Max30100 Pulse Oximeter Sensors (x3): Specialized sensors for measuring heart rate and blood oxygen levels.
• Multiplexer (MUX): Enables the ESP32 to interface with multiple Max30100 sensors efficiently.
• Power Supply: Provides the necessary power for the ESP32 and sensors.
• Wristband: Holds the sensors securely against the wrist for continuous monitoring.

System Operation

1. Pulse Waveform Sensing: The Max30100 sensors are strategically placed on the wrist to capture pulse waveforms associated with Vatta, Pitha, and Kapha.

2. Data Acquisition: The ESP32, equipped with the MUX, collects data simultaneously from the three Max30100 sensors. The MUX allows the ESP32 to switch between sensors efficiently.

3. Signal Processing: Raw pulse waveform data is processed in real-time to extract key parameters associated with Vatta, Pitha, and Kapha. Signal processing algorithms analyze the waveforms to identify characteristic patterns.

4. User Interface: A user-friendly interface, possibly a mobile app, provides a visual representation of Vatta, Pitha, and Kapha waveforms. Users can monitor their pulse patterns and receive insights into their Ayurvedic constitution.

Benefits

• Holistic Health Monitoring: The system provides a holistic approach to health monitoring based on Ayurvedic principles, offering insights into the balance of Vatta, Pitha, and Kapha.

• Continuous Monitoring: The wristband design ensures continuous monitoring, allowing for the observation of changes in pulse patterns over time.

Future Developments

• Integration with Machine Learning: Implementing machine learning algorithms for enhanced pattern recognition and personalized health insights.

• Connectivity Options: Expanding communication capabilities to integrate with cloud platforms for remote monitoring and data storage.

• User Education: Incorporating educational resources within the user interface to help individuals understand and interpret their pulse waveform data.

• Real-time Feedback: Users can receive real-time feedback on their pulse waveforms, enabling them to make lifestyle adjustments in accordance with Ayurvedic recommendations.