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Electromyography data for non-invasive naturally-controlled robotic hand prostheses.

Manfredo Atzori1, Arjan Gijsberts2, Claudio Castellini3

  • 1Information Systems Institute at the University of Applied Sciences Western Switzerland (HES-SO Valais) , Technoark 3, 3960 Sierre, Switzerland.

Scientific Data
|May 16, 2015
PubMed
Summary

Researchers created a benchmark database to improve non-invasive control of robotic prosthetic hands. This will help develop more natural and functional artificial hands for amputees.

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Area of Science:

  • Biomedical Engineering
  • Robotics
  • Neuroscience

Background:

  • Rehabilitation robotics shows promise for restoring hand function in amputees.
  • Current non-invasive prosthetic hand control, like myoelectric prostheses, offers limited capabilities and requires extensive training.
  • Existing research, while promising, has not yet met real-world needs for prosthetic hand control.

Purpose of the Study:

  • To bridge the gap between current research and real-world needs in prosthetic hand control.
  • To provide a benchmark scientific database for developing and testing algorithms for movement recognition and force control.
  • To facilitate the development of non-invasive, naturally controlled robotic hand prostheses.

Main Methods:

  • Development of a benchmark scientific database correlating surface electromyography (sEMG), hand kinematics, and hand forces.
  • Validation of the database by comparing acquired data to real-life conditions.
  • Application of state-of-the-art signal features and machine-learning algorithms for hand task recognition.

Main Results:

  • The created database is comparable to data acquired in real-life scenarios.
  • Successful recognition of various hand tasks using advanced signal processing and machine learning techniques.
  • Demonstrated feasibility of developing advanced control algorithms for prosthetic hands.

Conclusions:

  • The benchmark database is a valuable resource for advancing prosthetic hand research.
  • The findings support the potential for developing non-invasive, naturally controlled robotic prostheses.
  • Further development using this database can significantly improve the functionality of artificial hands for amputees.