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<p>Convenient and secure user identification is increasingly important in everyday environments, particularly with the proliferation of contactless interactions and Internet-of-Things (IoT) devices. However, conventional authentication methods often require explicit user input or additional hardware, limiting their usability in natural daily scenarios. To address this issue, we propose a doorknob-rotation-based user identification method using palm surface electromyography (sEMG). sEMG signals were acquired from the abductor pollicis brevis and abductor digiti minimi at 1,000&#xa0;Hz, denoised using a 60&#xa0;Hz notch and 20–500&#xa0;Hz band-pass filters, and transformed into time–frequency spectrograms via continuous wavelet transform. A DenseNet161 model was employed for classification. Using data from five participants, the proposed method achieved 94.00% test accuracy and 93.99% F1-score, with five-fold cross-validation accuracy of 91.66<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\:\pm\:\)</EquationSource> </InlineEquation>2.78%. The approach enables on-device, contact-based identification without wireless pairing, transforming everyday actions into seamless authentication. These results demonstrate the feasibility and practical potential of sEMG-based everyday-action user identification.</p>

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Palm sEMG-based user identification during doorknob rotation using a convolutional neural network

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月16日

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  • Yeonjung Shin,
  • Junghun Kim,
  • Sang-Il Choi

摘要

Convenient and secure user identification is increasingly important in everyday environments, particularly with the proliferation of contactless interactions and Internet-of-Things (IoT) devices. However, conventional authentication methods often require explicit user input or additional hardware, limiting their usability in natural daily scenarios. To address this issue, we propose a doorknob-rotation-based user identification method using palm surface electromyography (sEMG). sEMG signals were acquired from the abductor pollicis brevis and abductor digiti minimi at 1,000 Hz, denoised using a 60 Hz notch and 20–500 Hz band-pass filters, and transformed into time–frequency spectrograms via continuous wavelet transform. A DenseNet161 model was employed for classification. Using data from five participants, the proposed method achieved 94.00% test accuracy and 93.99% F1-score, with five-fold cross-validation accuracy of 91.66 \(\:\pm\:\) 2.78%. The approach enables on-device, contact-based identification without wireless pairing, transforming everyday actions into seamless authentication. These results demonstrate the feasibility and practical potential of sEMG-based everyday-action user identification.