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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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Palm sEMG-based user identification during doorknob rotation using a convolutional neural network.

Yeonjung Shin1, Junghun Kim2, Sang-Il Choi3

  • 1Department of Computer Software, Daegu Catholic University, Gyeongsan-si, Gyeongsangbuk-do, 38430, Republic of Korea.

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This study introduces a novel user identification method using palm surface electromyography (sEMG) signals captured during doorknob rotation. This approach offers secure, on-device authentication without extra hardware, transforming daily actions into seamless identification.

Keywords:
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Area of Science:

  • Biomedical Engineering
  • Human-Computer Interaction
  • Signal Processing

Background:

  • Secure user identification is crucial for contactless interactions and IoT devices.
  • Conventional authentication methods often require explicit user input or additional hardware, hindering natural daily use.
  • There is a need for seamless, integrated authentication solutions.

Purpose of the Study:

  • To propose and evaluate a doorknob-rotation-based user identification method.
  • To leverage palm surface electromyography (sEMG) for authentication.
  • To enable convenient and secure, on-device user identification.

Main Methods:

  • Acquired sEMG signals from specific hand muscles at 1,000 Hz.
  • Applied 60 Hz notch and 20-500 Hz band-pass filters for signal denoising.
  • Utilized continuous wavelet transform for time-frequency spectrograms and DenseNet161 for classification.

Main Results:

  • Achieved 94.00% test accuracy and 93.99% F1-score with five participants.
  • Demonstrated a five-fold cross-validation accuracy of 91.66 ± 2.78%.
  • Validated the feasibility of sEMG-based identification during everyday actions.

Conclusions:

  • The proposed method enables secure, on-device, contact-based identification.
  • It transforms everyday actions like doorknob rotation into seamless authentication.
  • This approach shows significant practical potential for integrated user identification systems.