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Asemi, Arsalan, maghooli, keivan, Nowshiravan Rahatabad, Fereidoun, Azadeh, Hamid. (1402). Survey of the Stability of Uniqueness of Muscle Synergy Patterns in Handwritten Signature over Time. نشریه علمی پژوهشی دانشگاه آزاد اسلامی, 17(3), 71-75. doi: 10.30486/mjee.2023.1973225.1009
Arsalan Asemi; keivan maghooli; Fereidoun Nowshiravan Rahatabad; Hamid Azadeh. "Survey of the Stability of Uniqueness of Muscle Synergy Patterns in Handwritten Signature over Time". نشریه علمی پژوهشی دانشگاه آزاد اسلامی, 17, 3, 1402, 71-75. doi: 10.30486/mjee.2023.1973225.1009
Asemi, Arsalan, maghooli, keivan, Nowshiravan Rahatabad, Fereidoun, Azadeh, Hamid. (1402). 'Survey of the Stability of Uniqueness of Muscle Synergy Patterns in Handwritten Signature over Time', نشریه علمی پژوهشی دانشگاه آزاد اسلامی, 17(3), pp. 71-75. doi: 10.30486/mjee.2023.1973225.1009
Asemi, Arsalan, maghooli, keivan, Nowshiravan Rahatabad, Fereidoun, Azadeh, Hamid. Survey of the Stability of Uniqueness of Muscle Synergy Patterns in Handwritten Signature over Time. نشریه علمی پژوهشی دانشگاه آزاد اسلامی, 1402; 17(3): 71-75. doi: 10.30486/mjee.2023.1973225.1009

Survey of the Stability of Uniqueness of Muscle Synergy Patterns in Handwritten Signature over Time

Majlesi Journal of Electrical Engineering
مقاله 9، دوره 17، شماره 3، آذر 2023، صفحه 71-75    اصل مقاله (472.38 K)
نوع مقاله: Reseach Article
شناسه دیجیتال (DOI): 10.30486/mjee.2023.1973225.1009
نویسندگان
Arsalan Asemi1؛ keivan maghooli* 2؛ Fereidoun Nowshiravan Rahatabad2؛ Hamid Azadeh3
1Department of Biomedical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
2Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
3Department of Physical Therapy, School of Rehabilitation Sciences, Isfahan University of Medical Sciences, Isfahan, Iran
چکیده
Biometric characteristics of the human body can play a decisive role in the accuracy of automatic signature verification systems due to their stability over time and resistance to variability in different conditions. In this study, the accuracy of an automatic handwritten signature verification system is checked for nine months. In this system, the electromyography (EMG) signals from the hand muscles of people during signing are recorded at different times up to nine months, and after the pre-processing of the signals, muscle synergy patterns are extracted by the non-negative matrix factorization (NMF) method. Finally, the patterns extracted by the SVM classifier are classified into two classes: genuine and forgery signatures.
کلیدواژه‌ها
Handwritten Signature؛ EMG؛ Muscle Synergy
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مراجع
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