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  3. Semg-based Hand Gesture Recognition Using Binarized Neural Network.
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  3. Semg-based Hand Gesture Recognition Using Binarized Neural Network.

Related Experiment Video

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

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sEMG-Based Hand Gesture Recognition Using Binarized Neural Network.

Soongyu Kang1, Haechan Kim1, Chaewoon Park1

  • 1School of Electronics and Information Engineering, Korea Aerospace University, Goyang-si 10540, Republic of Korea.

Sensors (Basel, Switzerland)
|February 11, 2023

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a novel hand gesture recognition (HGR) system using a single surface electromyography (sEMG) sensor and a binarized neural network (BNN). The system achieves high accuracy for dynamic gestures, enabling intuitive human-machine interfaces (HMI).

Keywords:
binarized neural networkfield-programmable gate arrayhand gesture recognitionspectrogramsurface electromyography

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Area of Science:

  • Biomedical Engineering
  • Machine Learning
  • Wearable Technology

Background:

  • Human-machine interfaces (HMI) are crucial for convenient device control.
  • Hand gesture recognition (HGR) systems offer intuitive interaction.
  • Surface electromyography (sEMG) sensors are advantageous for HGR due to environmental independence and low data requirements.

Purpose of the Study:

  • To develop a compact and efficient HGR system overcoming limitations of bulky multi-sensor setups and complex deep learning models.
  • To implement a lightweight convolutional neural network (CNN) using a binarized neural network (BNN) approach.
  • To integrate the system onto a field-programmable gate array (FPGA) for practical application.

Main Methods:

  • Utilized a single dry-type sEMG sensor for data acquisition.
  • Employed a binarized neural network (BNN), a lightweight CNN, for gesture classification.
  • Converted raw sEMG data into spectrograms for time-frequency domain analysis.
  • Implemented the system on a field-programmable gate array (FPGA).

    • Main Results:

    • Achieved 95.4% classification accuracy for nine dynamic gestures.
    • Demonstrated a low computation time of 14.1 ms.
    • Reported a power consumption of 91.81 mW, indicating high efficiency.

    Conclusions:

    • The proposed HGR system effectively recognizes dynamic gestures with high accuracy using minimal hardware.
    • The BNN-based approach on FPGA offers a practical solution for wearable HMI, balancing performance and resource constraints.
    • This technology has the potential to enhance user interaction across various applications.