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Muscle Cramp Detection System

A smart health monitoring system for early cramp detection using EMG, temperature, and oxygen saturation analysis.

Overview

This project implements a non-invasive muscle cramp detection system by integrating biomedical sensors, IoT, and machine learning techniques. The system continuously monitors EMG signals, skin temperature, and tissue oxygen saturation (StO₂), processes the data using MATLAB on ThingSpeak, and provides real-time alerts.

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System Architecture

1️⃣ Signal Acquisition

• EMG Sensor: Captures muscle activity.
• IR Temperature Sensor: Measures skin temperature.
• Photodiode + Linear Regression: Estimates StO₂ by calculating IR intensity ratio (hardcoded values).

2️⃣ Signal Processing

• The EMG signal undergoes noise filtering using an analog front-end circuit.
• The StO₂ value is derived using a hardcoded linear regression model.

3️⃣ Data Transmission & Analysis

• The ESP32 microcontroller sends real-time EMG, temperature, and StO₂ data to ThingSpeak IoT platform.
• MATLAB scripts on ThingSpeak process the signals and determine cramp probability.

4️⃣ Cramp Detection & Alert System

• If a cramp is detected, the ESP32 triggers a buzzer and displays the cramp ratio on an LCD screen.

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Workflow Diagram

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graph TD;
  A[EMG Sensor] -->|Analog Filtering| B[ESP32]
  C[IR Temp Sensor] --> B
  D[Photodiode] -->|Linear Regression| B
  B -->|Wi-Fi| E[ThingSpeak IoT]
  E -->|MATLAB Analysis| F[Cramp Detection]
  F -->|Result| G[ESP32]
  G -->|Cramp Detected?| H{Yes/No}
  H --Yes--> I[Buzzer Alert & Display]

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Hardware Components

✔️ EMG Sensor
✔️ IR Temperature Sensor
✔️ Photodiode
✔️ ESP32
✔️ LCD Display
✔️ Buzzer

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Technologies Used

🔹 Embedded C (ESP32 Firmware)
🔹 MATLAB (Signal Processing on ThingSpeak)
🔹 ThingSpeak IoT Platform (Cloud Analysis)
🔹 LTSpice (Circuit Simulation)
🔹 Linear Regression (StO₂ Estimation)

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Final Product Image

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How to Use

1. Power on the ESP32-based device.
2. Wear the EMG and temperature sensors on the target muscle area.
3. Data is transmitted to ThingSpeak in real-time.
4. MATLAB analysis determines the likelihood of a cramp.
5. If cramp detected:
   • The buzzer sounds an alert.
   • The cramp ratio is displayed on the LCD.

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Future Enhancements

✅ Improve StO₂ accuracy using optimized machine learning models.
✅ Develop a mobile app for real-time cramp monitoring.
✅ Integrate Bluetooth support for offline data analysis.

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Contributors

• Thamilezai Ananthakumar
• Dayananthan
• Prathishanth

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References

🔗 ThingSpeak IoT Platform
🔗 ESP32 Documentation

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🚀 This project bridges IoT, Signal Processing, and biomedical engineering for real-time muscle health monitoring.