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September 16, 2025The International Journal of Artificial Organs

Empowering healthcare: Secure hand gesture authentication in medical IoT with sEMG

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Authors

PVP VenkateswariRNR. NagendranMRM. Rohini

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Overview

Novel method enhances biometric security through sEMG signals for authentication, indicating improved privacy protection.

Key Points

  • The proposed method achieves an accuracy of 99.72% using sEMG signals for user authentication.
  • A cancellable biometric token increases security by allowing users to reset compromised tokens when necessary.
  • The study utilizes a multi-stage process that includes data capture, feature extraction, and machine learning for enhanced verification.
  • Experimental validation demonstrates the method's high performance with an F1-score of 96.0% and an Equal Error Rate of 0.0037.

Cite This Study

Venkateswari et al. (2025) studied this question.

synapsesocial.com/papers/68d44f8331b076d99fa570dbhttps://doi.org/10.1177/03913988251370224
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A Multimodal benchmark of EMG and 3D hand motion for gesture recognition and biometric authentication2026
  2. 2Hand Gesture Classification using sEMG Signals and Ensemble Learning2024
  3. 3A novel biometric authentication approach using ECG and EMG signals2015 · 78 citations
  4. 4Hand gestures classification of sEMG signals based on BiLSTM- Metaheuristic Optimization and Hybrid U-Net-MobileNetV2 Encoder Architecture2024 · 3 citations
  5. 5Securing Internet-of-Medical-Things networks using cancellable ECG recognition2024 · 27 citations