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Finger Angle Estimation From Array EMG System Using Linear Regression Model With Independent Component Analysis.

Sorawit Stapornchaisit1, Yeongdae Kim1, Atsushi Takagi2,3

  • 1Department of Information Processing, Tokyo Institute of Technology, Tokyo, Japan.

Frontiers in Neurorobotics
|October 17, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces array EMG sensors and Independent Component Analysis (ICA) to improve prosthetic hand control. This method enhances signal quality and enables more natural finger motion compared to traditional approaches.

Keywords:
adaptive mixture ICA (AMICA)array EMG systemconvolutional pose machinesfinger motionmultichannel surface EMGmusculoskeletal modelssurface ElectroMyoGraphy (EMG)

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Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Robotics

Background:

  • Surface electromyography (EMG) signals are crucial for prosthetic hand control.
  • Accurate EMG signal acquisition is challenging due to small, deep forearm muscles and sensor placement variability.
  • Current prosthetic hands often use limited muscle patterns, restricting natural finger movement.

Purpose of the Study:

  • To enhance prosthetic hand and finger control by improving EMG signal quality and enabling natural motion.
  • To address limitations of conventional EMG sensor placement and muscle identification in amputees.
  • To develop a method for precise estimation of individual finger joint angles.

Main Methods:

  • Utilized array EMG sensors for comprehensive forearm signal acquisition.
  • Applied Independent Component Analysis (ICA) to isolate relevant signal components.
  • Employed linear regression models (LRM) to estimate finger joint angles from processed EMG signals.
  • Validated results using a mathematical musculoskeletal model (MSM).

Main Results:

  • Array EMG sensors combined with ICA significantly improved signal quality and signal-to-noise ratio.
  • The ICA method demonstrated a higher correlation coefficient (CC) compared to conventional EMG methods, indicating reduced noise.
  • Estimated finger joint angles using the ICA-enhanced method showed strong linear relationships (average CC > 0.7) with actual movements.
  • The system successfully enabled separate control of individual finger angles for flexion and extension.

Conclusions:

  • Array EMG sensors and ICA offer a robust solution for acquiring high-quality EMG signals from the forearm.
  • This approach overcomes limitations of traditional EMG methods, improving prosthetic control accuracy and precision.
  • The developed technique facilitates more natural and intuitive prosthetic finger motion, enhancing user experience.