CyRa 🎯

Basic Details

Team Name: Debuggers

Team Members

• Member 1: Laasya UG - MITS
• Member 2: Shivani S Bhat - MITS

Project Description

CyRa is a smart wearable menstrual health tracking system that uses real-time biometric sensing and machine learning to monitor and predict a user’s menstrual cycle phases with high accuracy. Instead of manual input or calendar-based estimations, the system continuously collects physiological data such as basal body temperature, heart rate, and motion patterns using body-mounted sensors.

The wearable device transmits sensor data to a cloud-based backend, where a machine learning model analyses patterns to predict cycle phases, ovulation windows, and upcoming periods. Results appear on an interactive web dashboard that offers personalised health insights, symptom tracking, and phase-based wellness guidance.

By combining hardware sensing, data analytics, and personalised recommendations, CycleAura offers users a comprehensive and automated understanding of their reproductive health.CycleAura_ProjectPlan

The Problem statement

Most existing menstrual tracking apps rely on calendar-based predictions and manual input, which ignore real physiological signals and lead to inaccurate results. This prevents users from receiving reliable ovulation predictions and personalised health insights. There is a need for an automated system that uses real biometric data to provide accurate, individualised cycle tracking.

The Solution

CyRa solves this problem by using a wearable device that continuously collects real biometric data such as basal body temperature, heart rate, and motion. This data is analysed using machine learning to accurately predict menstrual cycle phases, ovulation, and upcoming periods. The system then delivers personalised insights and health guidance through an interactive web dashboard.

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Technical Details

Technologies/Components Used

For Software:

• Languages used: Arduino C++, Python, Javascript, HTML & CSS

• Frameworks used: Flask, React, MongoDB, Socket.io, scikit-learn

• Libraries used:

  • 🔹 Firmware (ESP32)

  • OneWire — communication with DS18B20 temperature sensor
  • DallasTemperature — temperature data processing
  • Wire (I2C) — communication with OLED and MAX30102
  • Adafruit GFX — graphics support for OLED display
  • Adafruit SSD1306 — OLED display control
  • MAX30102 Sensor Library — heart rate and SpO2 readings
  • WiFi — network connectivity
  • HTTPClient — sending sensor data to backend

  🔹 Backend (Python)

  • Flask — REST API server
  • pymongo — MongoDB database connection
  • scikit-learn — machine learning model
  • pandas — data processing and analysis
  • flask-cors — cross-origin request handling
  • python-socketio — real-time communication

  🔹 Frontend (React)

  • Axios — API requests
  • Recharts — data visualization graphs
  • socket.io-client — live sensor data updates

• Tools used: VS code, MongoDB Atlas

For Hardware:

• Main components: ESP-32 Dev Board V1, MAX30102 (Heart Rate monitor), ds18b20 (Temperature Sensor), ADXL335 (Accelometer)

• Specifications:

  Component	Specification	Purpose
  ESP32	240 MHz dual-core processor, WiFi enabled	Data processing and cloud communication
  DS18B20	Digital temperature sensor, ±0.5°C accuracy	Basal body temperature tracking
  MAX30102	Optical heart rate and SpO₂ sensor	Pulse and oxygen monitoring
  ADXL335	3-axis analog accelerometer	Motion artifact removal

• Tools required: Arduino IDE

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Features

List the key features of your project:

• Feature 1: Real-Time Biometric Tracking - Continuously monitors basal body temperature, heart rate, and motion using wearable sensors.
• Feature 2: AI-Based Cycle Prediction - Machine learning analyzes personal biometric patterns to predict cycle phases and ovulation accurately.
• Feature 3: Non-Invasive Wearable Design - Comfortable sensor-based device enables effortless daily health monitoring without manual input.
• Feature 4: Personalized Health Insights Dashboard - Interactive web app provides cycle predictions, symptom tracking, and phase-based wellness guidance.

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Implementation

For Software:

Installation

pip install -r requirements.txt

Run

npm run dev
python app.py

For Hardware:

Components Required

Component	Specification	Purpose
ESP32	240 MHz dual-core processor, WiFi enabled	Data processing and cloud communication
DS18B20	Digital temperature sensor, ±0.5°C accuracy	Basal body temperature tracking
MAX30102	Optical heart rate and SpO₂ sensor	Pulse and oxygen monitoring
ADXL335	3-axis analog accelerometer	Motion artifact removal

Circuit Setup

1. Connect DS18B20 Temperature Sensor

   • VCC → 3.3V (ESP32)
   • GND → GND
   • DATA → GPIO 4 (ESP32)
   • Add a 4.7kΩ pull-up resistor between DATA and VCC

2. Connect MAX30102 Heart Rate Sensor (I2C)

   • VCC → 3.3V (ESP32)
   • GND → GND
   • SDA → GPIO 21 (ESP32)
   • SCL → GPIO 22 (ESP32)

3. Connect ADXL335 Accelerometer

   • VCC → 3.3V (ESP32)
   • GND → GND
   • X → GPIO 34 (ESP32, ADC)
   • Y → GPIO 35 (ESP32, ADC)
   • Z → GPIO 32 (ESP32, ADC)

4. Power the ESP32

   • Connect ESP32 to computer using USB cable.

5. Upload Firmware

   • Open Arduino IDE
   • Select ESP32 board and correct COM port
   • Upload firmware to start data collection

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Project Documentation

For Software:

Screenshots

![Screenshot1] : Dashboard

![Screenshot2] : Signin

![Screenshot3]: signup

Diagrams

System Architecture:

Application Workflow:

![Workflow]:

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For Hardware:

Schematic & Circuit

![Schematic]

Build Photos:

![Components]

• ESP 32

  

  

  

  



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Additional Documentation

For Web Projects with Backend:

Endpoints

🔗 Backend API Endpoints

Sensor Data

Method	Endpoint	Payload / Response	Purpose
POST	/api/sensor/bbt	{ bbt, timestamp }	Receive basal body temperature reading from ESP32
POST	/api/sensor/heartrate	{ bpm, spo2, timestamp }	Receive heart rate and SpO₂ data from ESP32

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Cycle Prediction & Health Data

Method	Endpoint	Payload / Response	Purpose
GET	/api/cycle/prediction	Returns phase, confidence, ovulation date, next period	Get ML cycle prediction
GET	/api/cycle/history	Returns array of past BBT readings	BBT trend graph data
GET	/api/cycle/heartrate	Returns array of past BPM readings	Heart rate trend data

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Journal & Insights

Method	Endpoint	Payload / Response	Purpose
POST	/api/journal/entry	{ mood, pain, flow, pms, energy, notes }	Save journal entry
GET	/api/journal/entries	Returns list of journal entries	Retrieve journal history
GET	/api/tips/today	Returns tips for current phase	Provide personalized health tips

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User Profile

Method	Endpoint	Payload / Response	Purpose
POST	/api/user/profile	{ age, bmi, pcos, birth_control }	Save user profile
GET	/api/user/profile	Returns user profile object	Load profile for ML personalization

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For Hardware Projects:

Bill of Materials (BOM)

Component	Quantity	Specifications	Price (INR)	Link/Source
ESP32 Development Board	1	WiFi-enabled microcontroller, 240 MHz dual-core	400	Local electronics store / online
DS18B20 Temperature Sensor	1	Digital temperature sensor, ±0.5°C accuracy	100	Local electronics store / online
MAX30102 Heart Rate Sensor	1	Optical heart rate and SpO₂ sensor (I2C)	300	Local electronics store / online
ADXL335 Accelerometer	1	3-axis analog accelerometer	200	Local electronics store / online
Breadboard	1	Standard solderless breadboard	80	Local electronics store / online
Jumper Wires	1 set	Male-to-male / male-to-female wires	70	Local electronics store / online
USB Cable	1	USB to Micro-USB for ESP32	100	Local electronics store / online

Total Estimated Cost: ₹[Amount]

Assembly Instructions

Step 1: Prepare Components

1. Gather all components listed in the BOM
2. Check component specifications
3. Prepare your workspace Caption: All components laid out

Step 2: Build the Power Supply

1. Connect the power rails on the breadboard
2. Connect Arduino 5V to breadboard positive rail
3. Connect Arduino GND to breadboard negative rail Caption: Power connections completed

Step 3: Add Components

1. Place LEDs on breadboard
2. Connect resistors in series with LEDs
3. Connect LED cathodes to GND
4. Connect LED anodes to Arduino digital pins (2-6) Caption: LED circuit assembled

Step 4: [Continue for all steps...]

Final Assembly: Caption: Completed project ready for testing

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For Scripts/CLI Tools:

Command Reference

Basic Usage:

python app.py
npm run dev

Available Commands:

Example 1: Start backend server

python app.py

Example 2: Start frontend dashboard

npm run dev

Example 3: Run backend on custom port

python app.py --port 8000

Example 4: Test API endpoint

curl http://localhost:5000/api/cycle/prediction

Demo Output

Example 1: Basic Processing

Input:

ESP32 collects biometric readings
BBT = 36.6°C
Heart Rate = 72 BPM
Motion Level = Low

Command:

python app.py

Output:

{"confidence":0.572,"cycle_length_est":28.0,"health_score":9.7,"next_period_in_days":27,"ovulation_positive":true,"phase":"Menstrual"}

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Project Demo

Video

https://drive.google.com/file/d/1DEAxZTcOSHJu2X62a3U3_ma0BSdFjmmE/view?usp=sharing Hardware Features • Daily BBT reading via DS18B20 — most important input for ML (53.9% feature importance) • Real-time heart rate and SpO2 via MAX30102 • Motion Artifact Removal via ADXL335 — near medical-grade HR accuracy • OLED shows: cycle phase, BBT reading, live BPM, WiFi status • WiFi sync to cloud every 60 seconds

Dashboard (Home Page) • Animated cycle phase wheel — Menstrual (red), Follicular (yellow), Ovulatory (green), Luteal (blue) • Live BBT card — today's reading with trend arrow • Live heart rate and SpO2 card — updates every 60 seconds via Socket.io • ML prediction card — current phase, confidence %, ovulation status • Days until next period countdown • Predicted health score out of 10 • Today's phase-based tip card

Insights Page • 30-day BBT trend graph with phase color-coding • Heart rate history graph • Ovulation window highlighted on cycle timeline • Cycle length history chart • Next predicted cycle length • Health score trend over time

Journal Page • Mood selector — emoji-based (1-10 mapped to happy to very sad) • Pain level slider (1-10) • Energy level slider (1-10) • Flow level selector (Light / Moderate / Heavy) • PMS symptoms checkbox • Free text notes • Past entries calendar view

Tips Page • Phase-specific diet recommendations (e.g. iron-rich foods in menstrual phase) • Exercise suggestions (e.g. light yoga in menstrual, HIIT in follicular) • Sleep hygiene tips per phase • Emotional wellness advice

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AI Tools Used

If you used AI tools during development, document them here for transparency:

Tool Used: Claude, ChatGPT

Purpose:

• Assisted in structuring the project README documentation
• Helped generate boilerplate backend API structure
• Provided debugging guidance for sensor integration and data flow
• Suggested improvements for system architecture and feature design

Key Prompts Used:

• "Generate a Flask API structure for IoT sensor data"
• "Explain how to integrate ESP32 sensor data with a web dashboard"
• "Create README sections for hardware-software integrated project"
• "Improve project documentation for hackathon submission"

Percentage of AI-generated code: Approximately 15–20%

Human Contributions:

• Hardware integration and circuit design
• Sensor interfacing and firmware implementation
• Machine learning model training and integration
• Backend and frontend development
• System testing and validation
• UI/UX design and project presentation

Note: AI tools were used only for assistance and documentation support. All core implementation and design decisions were completed by the team.

Team Contributions

• Laasya UG :
  • Backend development and ML integration
  • Frontend dashboard development
  • Database setup and testing
• Shivani S Bhat:
  • Hardware integration and sensor interfacing
  • Firmware development for ESP32
  • System architecture design

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License

This project is licensed under the [LICENSE_NAME] License - see the LICENSE file for details.

Common License Options:

• MIT License (Permissive, widely used)
• Apache 2.0 (Permissive with patent grant)
• GPL v3 (Copyleft, requires derivative works to be open source)

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