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Updated: May 14, 2025

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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A Fast and Low-Impact Embedded Orientation Correction Algorithm for Hand Gesture Recognition Armbands.

Andrea Mongardi1, Fabio Rossi1, Andrea Prestia1

  • 1Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy.

Sensors (Basel, Switzerland)
|April 12, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new algorithm to correct sensor displacement in surface ElectroMyoGraphy (sEMG) armband systems for hand gesture recognition. The method ensures high accuracy regardless of armband orientation, improving wearable Human-Machine Interfaces (HMIs).

Keywords:
embedded algorithmhand gesture recognitionhuman–machine interfacesurface electromyographywearable armband

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Area of Science:

  • Biomedical Engineering
  • Human-Computer Interaction
  • Signal Processing

Background:

  • Surface ElectroMyoGraphy (sEMG) is crucial for wearable Human-Machine Interfaces (HMIs) in hand gesture recognition.
  • Sensor placement significantly affects the performance of sEMG-based systems, posing a challenge for reliable HMI applications.

Purpose of the Study:

  • To develop a fast, low-impact orientation correction algorithm for sEMG-based HMI armbands to address sensor displacement.
  • To enhance the robustness and accuracy of hand gesture recognition systems independent of armband orientation.

Main Methods:

  • Introduced a novel algorithm featuring a calibration phase for armband orientation estimation and real-time data correction.
  • The algorithm requires only two distinct hand gestures for sEMG activation, ensuring hardware and database independence.
  • Implemented the algorithm in a seven-channel sEMG armband system utilizing an Artificial Neural Network (ANN) for nine-gesture recognition.

Main Results:

  • Achieved an average prediction accuracy of 93.36% even with arbitrary armband wearing orientations.
  • Demonstrated minimal impact on power consumption (additional 500 μW) and latency (increase of 408 μs).
  • Validated the algorithm's effectiveness, general applicability, and efficiency in correcting electrode-shift issues.

Conclusions:

  • The developed orientation correction algorithm offers a promising solution for improving the reliability of sEMG-based HMIs.
  • The algorithm's efficiency and independence from retraining models make it highly suitable for practical HMI applications.