Resnet1d-based personal identification with multi-session surface electromyography for electronic health record integration

HIGHLIGHTS

• What: The paper provides a comprehensive overview of proficient EMG characterization and highlights state-of-the-art advancements in this domain, specifically focusing on neuromuscular pathologies. The authors investigated the feasibility of using electromyogram (sEMG) readings and advanced machine_learning techniques for personal identification within an electronic health record (EHR) access control mechanism. Signal processing is an essential stage of the sEMG signal analysis, aiming to remove unwanted noise and interference to obtain a cleaner and more informative signal for further processing and analysis. The acceptable standards for accuracy, precision, recall, and F1 score depend on various factors . . .
  

   

  

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