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Related Experiment Video

Updated: Apr 29, 2026

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Embedded Machine Learning System for Muscle Patterns Detection in a Patient with Shoulder Disarticulation.

Erick Guzmán-Quezada1, Claudia Mancilla-Jiménez2, Fernanda Rosas-Agraz1,3

  • 1Departamento de Electromecánica, Universidad Autónoma de Guadalajara, Guadalajara 45129, Mexico.

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Summary
This summary is machine-generated.

This study developed a portable AI system for real-time electromyographic (EMG) signal classification to control prosthetic devices. The system accurately interprets muscle activity, enhancing prosthetic functionality and user experience.

Keywords:
Edge Impulse platformartificial intelligenceelectromyographic signalsportable systemprosthetic control systemsshoulder joint movements

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence
  • Rehabilitation Technology

Background:

  • Advancements in artificial intelligence (AI) are crucial for developing sophisticated control systems for prosthetic devices.
  • Electromyographic (EMG) signal classification is key to enabling intuitive and responsive prosthetic control.
  • Existing systems often face limitations in portability and real-time processing capabilities.

Purpose of the Study:

  • To develop and evaluate a portable AI-based system for real-time EMG signal classification.
  • To enable actuator control for prosthetic devices using shoulder muscle activity.
  • To enhance the autonomy and functionality of prosthetic limbs.

Main Methods:

  • Utilized low-power microcontrollers for continuous EMG signal recording.
  • Employed the Edge Impulse platform for AI model development and deployment on edge devices.
  • Conducted a case study involving a patient with left shoulder disarticulation, collecting EMG data over two days for training and real-time testing.

Main Results:

  • The AI system demonstrated accurate and swift real-time classification of EMG signals.
  • The system successfully translated muscle electrical activity into control commands for prosthetic actuators.
  • The portable design facilitated continuous signal recording and enhanced user mobility.

Conclusions:

  • Portable AI-based systems show significant potential for advanced prosthetic control.
  • Real-time EMG signal classification is feasible and can improve prosthetic user functionality.
  • This technology offers a pathway to enhance the quality of life for individuals using prosthetic devices.