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Related Experiment Video

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A Real-Time Wearable Electromyography Measurement System for Small Animals
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A Multiday Evaluation of Real-Time Intramuscular EMG Usability with ANN.

Asim Waris1, Muhammad Zia Ur Rehman2, Imran Khan Niazi3,4,5

  • 1Department of Biomedical Engineering and Sciences, School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan.

Sensors (Basel, Switzerland)
|June 19, 2020
PubMed
Summary

Intramuscular electromyography (iEMG) signals recorded over five days show that combining data across multiple days significantly improves real-time control performance for prosthetic limbs. This approach enhances robustness and stability in myoelectric control systems.

Keywords:
intramuscular electromyography (iEMG)pattern recognition (PR)prosthetic hand

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Area of Science:

  • Biomedical Engineering
  • Neuroprosthetics
  • Signal Processing

Background:

  • Advancements in implantable technology enable high-density recordings and wireless signal transmission for prosthetic control.
  • Intramuscular electromyography (iEMG)-based myoelectric control offers a promising future for enhanced prosthetic functionality.

Purpose of the Study:

  • To investigate the real-time control performance of iEMG signals over an extended period (five days).
  • To quantify the robustness of real-time performance parameters using a novel protocol.
  • To evaluate different training and testing strategies for iEMG signal classification.

Main Methods:

  • Intramuscular wires were used for iEMG signal recording, remaining implanted for five consecutive days.
  • Fitts' law was employed for performance evaluation, measuring throughput, completion rate, path efficiency, and overshoot.
  • An artificial neural network (ANN) classifier was trained and tested using three schemes: same-day (WDT), previous-day (BDT), and cumulative-day (CDT) data.

Main Results:

  • The cumulative-day training (CDT) strategy yielded a significantly higher completion rate (91.6%) compared to same-day (WDT, 88.16%) and previous-day (BDT, 74.02%) strategies (p < 0.01).
  • For BDT, performance varied significantly across daily sessions, with the first session outperforming subsequent ones.
  • Subjects successfully achieved targets using wire electrodes, demonstrating feasibility.

Conclusions:

  • Time variations in iEMG signals can be effectively managed by concatenating data over several days.
  • The proposed cumulative data training scheme enhances the stability and robustness of iEMG-based myoelectric control.
  • This approach holds potential for improving the reliability of future prosthetic control systems.