EMG-based Biometric Approaches using Machine Learning Models: A Concise Survey

Authors

DOI:

https://doi.org/10.5281/zenodo.8070742

Keywords:

EMG, Biometric, Machine Learning, Survey

Abstract

Biometric recognition systems offer technology that recognizes an individual based on their body's unique characteristics. This technology verifies the identity of the host by analyzing a person's physical characteristics and determining if they match the host data stored in the database. For example, authentication systems analyze features such as fingerprint or iris pattern, or host's face shape. However, a person's identity can be verified by analyzing the gait or the characteristic frequency of the sound using an accelerometer integrated in the user's smartphone. The most promising biometric technology in recent years is the use of unique signals such as electromyogram (EMG). This method provides real-time authentication by analyzing the morphological features of EMG signals. Thus, it has a significant development potential in the field of biometric technology, as it prevents hacking. In this literature summary, EMG-based biometric studies are described.

Author Biography

  • Ilyas OZER, Bandırma Onyedi Eylül Üniversitesi, Elektrik-Elektronik Mühendisliği

    Bilgisayar Mühendisliği

References

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Published

17-06-2022

Issue

Vol. 1 No. 1 (2022): Aintelia® Science Notes | Volume 1, Issue 1 15-17 June, 2022

Section

Articles

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How to Cite

Görür, K., OZER, I., Çetin, O., & TEMURTAŞ, F. (2022). EMG-based Biometric Approaches using Machine Learning Models: A Concise Survey. AINTELIA Science Notes Journal, 1(1). https://doi.org/10.5281/zenodo.8070742