🦾 Muscle Cramp Detector - STEM Group

📌 Overview

Muscle cramps can be unpredictable and cause discomfort or pain, especially for athletes, workers, and patients with neuromuscular conditions.
This project focuses on developing an analog-based EMG filtering system that captures and processes EMG signals using hardware filters and an ESP32 to analyze muscle activity in real-time.

The system filters and transmits the EMG signal to ThingSpeak IoT Platform for advanced time-domain & frequency-domain analysis. Since ThingSpeak has private channel restrictions, we also built a custom website for real-time signal visualization, making this a mini web-based oscilloscope.

---

🛠️ Hardware & Components Used

🔹 EMG Signal Acquisition

• Dry Electrodes - Used to capture muscle signals non-invasively.
• Instrumentation Amplifier - High CMRR, low-noise signal amplification.

🔹 Analog Signal Processing

• 4th-Order Low-Pass Filter - Removes high-frequency noise.
• 2nd-Order High-Pass Filter - Eliminates DC offset and low-frequency artifacts.
• Notch Filter (50/60Hz) - Suppresses power line interference.
• TL702 Op-Amp - Selected for high slew rate, high CMRR, and power rejection ratio.
• Potentiometers - Allow adjustable gain and cutoff frequency tuning.

🔹 IoT & Visualization

• ESP32 - Transmits processed signals to ThingSpeak.
• ThingSpeak IoT Platform - Converts time-domain signals to frequency-domain for analysis.
• Custom Website - Due to ThingSpeak's private channel restrictions, we designed a web-based oscilloscope to visualize both time-domain & frequency-domain EMG data.

---

📊 Signal Processing Flow

🔹 EMG Signal Processing Flow

graph TD;
  A[EMG Dry Electrodes] -->|Analog Filtering| B[ESP32 Microcontroller]
  B -->|Wi-Fi Transmission| C[ThingSpeak IoT Platform]
  C -->|FFT & Feature Extraction| D[Custom Website]
  D -->|Real-Time Signal Display| E[User Visualization]

Loading

---

🎯 Features

✅ Real-time EMG signal acquisition
✅ Analog-based filtering system for clean signal extraction
✅ ThingSpeak IoT integration for advanced signal processing
✅ Custom web-based oscilloscope for visualization
✅ Adjustable filtering parameters with potentiometers
✅ Potential for further feature extraction & classification

---

🖼️ Images & Demonstrations

📌 1. Breadboard Implementation

📌 2. Internal Circuit Structure

📌 3. Final PCB Implementation

📌 4. Final Product Implementation

📌 5. Web-Based Oscilloscope Display

---

👨‍💻 Contributors (STEM Group)

• Thamilezai Ananthakumar
• Thusajiny Ahilakumaran
• Sivamynthan Nadesamoorthy
• Mathujan Shanmugavadivel

---

🚀 Future Plans

🔹 Enhanced Signal Processing: Implementing more feature extraction techniques.
🔹 Improved Web Interface: Adding interactive visualization tools.

---