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<p>As gender recognition is key in advancing personalized medicine, this study explores the use of high-density surface electromyography (HD-sEMG) signals for gender recognition during the Sit-to-Stand (STS) exercise, utilizing a combination of time, frequency, and time-frequency domain features with machine learning classifiers. A comprehensive methodology is presented, including signal preprocessing, feature extraction, and classification through conventional classifiers (K-NN, SVM, LR, DT, RF) and a hybrid CNN-KNN model, leveraging the Stockwell Transform for time-frequency image representation of the signal. Data from 64 participants across five age groups were analyzed using a 5-fold cross-validation process to ensure robustness. The CNN-KNN model achieved the highest accuracy of <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(99.08 \%\)</EquationSource> </InlineEquation> ± 1.12, significantly outperforming traditional models. Additionally, the study highlights the impact of aging on gender recognition, underscoring the importance of age-aware models for accurate predictions. This work demonstrates the potential of HD-sEMG signals for both clinical and biometric applications involving gender and age-specific analysis.</p>

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Gender recognition with aging using HD-sEMG signals

  • 期刊论文

  • 发表日期：

    2026年7月21日

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  • Sidi Mohamed Sid’El Moctar,
  • Honglei Zhang,
  • Imad Rida,
  • Kiyoka Kinugawa,
  • Sofiane Boudaoud

摘要

As gender recognition is key in advancing personalized medicine, this study explores the use of high-density surface electromyography (HD-sEMG) signals for gender recognition during the Sit-to-Stand (STS) exercise, utilizing a combination of time, frequency, and time-frequency domain features with machine learning classifiers. A comprehensive methodology is presented, including signal preprocessing, feature extraction, and classification through conventional classifiers (K-NN, SVM, LR, DT, RF) and a hybrid CNN-KNN model, leveraging the Stockwell Transform for time-frequency image representation of the signal. Data from 64 participants across five age groups were analyzed using a 5-fold cross-validation process to ensure robustness. The CNN-KNN model achieved the highest accuracy of \(99.08 \%\) ± 1.12, significantly outperforming traditional models. Additionally, the study highlights the impact of aging on gender recognition, underscoring the importance of age-aware models for accurate predictions. This work demonstrates the potential of HD-sEMG signals for both clinical and biometric applications involving gender and age-specific analysis.