SquatScope

SquatScope

Real-time EMG and IMU Monitoring System for Exercise Analysis

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Background

SquatScope is a comprehensive wearable sensor system designed for real-time monitoring and analysis of muscle activity (EMG) and motion (IMU) during exercise activities. The system aims to provide athletes, researchers, and fitness enthusiasts with detailed insights into their movement patterns and muscle activation.

Focus on Aging Population and Quadriceps Muscle Health

As the global population ages, muscle strength decline becomes a critical health concern, particularly affecting the quadriceps muscles of the thigh. The quadriceps muscle group plays an essential role in:

• Fall Prevention: Strong quadriceps muscles provide knee stability and control during dynamic movements, significantly reducing fall risk in elderly individuals
• Gait Maintenance: Adequate quadriceps strength is fundamental for maintaining normal walking patterns and mobility independence
• Activities of Daily Living (ADL): Quadriceps function is crucial for basic movements such as standing up from chairs, climbing stairs, and maintaining balance during daily activities

The project addresses the need for portable, high-frequency data collection and real-time visualization of biomechanical data during exercises like squats, providing immediate feedback on form and muscle engagement. This is particularly valuable for monitoring and improving quadriceps muscle function in aging populations, enabling early intervention strategies to maintain mobility and independence.

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System Architecture

Hardware Components

Wearable Device Side

• Power Supply: Two 9V batteries
• Communication: Bluetooth Low Energy (BLE)
• Sampling Rate: 1000Hz for both IMU and EMG data

PC Application Side

• Platform: Laptop/PC with BLE capability
• Operating System: Cross-platform (Windows, macOS, Linux)
• Communication: BLE receiver for data acquisition

Key Software Components

1. Wearable Device (Arduino/ESP32)

• IMU data acquisition with calibration
• EMG signal sampling
• BLE data transmission with packet batching
• Real-time display on device screen

2. PC Application (Python)

• `BLEManager`: Handles BLE connection and data reception
• `DataProcessor`: Processes raw sensor data with filtering
• `MainWindow`: Real-time visualization interface
• `SensorData`: Data model for sensor information

3. Data Analysis Pipeline

• High-pass, low-pass, and notch filtering
• RMS calculation for muscle activation intensity
• Frequency domain analysis
• Bilateral muscle activity comparison
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Demo and Usage

Getting Started

1. Hardware Setup

• Attach EMG electrodes to target muscle groups
• Power on M5Stick C Plus 2
• Ensure 9V battery power supply is connected

2. Software Launch

python main.py

3. Connection Process

• Click "Connect" button in the application
• System automatically scans for "M5-IMU-EMG-1000Hz" device
• Establishes BLE connection

User Interface Features

Real-time Monitoring Dashboard

• Connection Status: Live BLE connection indicator
• Data Rate Display: Shows packet reception rate and data statistics
• Multi-channel Visualization: Separate graphs for:
• 3-axis accelerometer data
• 3-axis gyroscope data
• EMG signal amplitude

Interactive Controls

• Start/Stop Recording: Begin and end data collection sessions
• Calibration: IMU sensor calibration using device button
• Data Export: Save recorded sessions for later analysis
• Filter Settings: Adjustable signal processing parameters

Analysis Features

• Real-time Signal Processing:
• Butterworth filtering (high-pass: 20Hz, low-pass: 500Hz)
• Power line noise removal (48-52Hz notch filter)
• RMS calculation for muscle activation intensity
• Exercise Metrics:
• Muscle activation onset detection
• Bilateral muscle balance assessment
• Movement pattern analysis through IMU data

Typical Workflow

1. Setup Phase: Attach sensors and establish connection

2. Calibration: Perform IMU calibration for accurate measurements

3. Exercise Execution: Perform target exercise (e.g., squats) while monitoring real-time data

4. Analysis: Review captured data with built-in analysis tools

5. Export: Save session data for further analysis or documentation

The system provides immediate visual feedback, allowing users to adjust their form and technique in real-time based on muscle activation patterns and movement data.