Majlesi Journal of Electrical Engineering Survey of the Stability of Uniqueness of Muscle Synergy Patterns in Handwritten Signature over Time Volume 17 (2023) Issue 3, September 2023 2024 02 03 Survey of the Stability of Uniqueness of Muscle Synergy Patterns in Handwritten Signature over Time 10.30486/mjee.2023.1973225.1009 EN Arsalan Asemi Department of Biomedical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran 0000-0001-5312-1031 Arsalan Asemi Department of Biomedical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran 0000-0001-5312-1031 Fereidoun Nowshiravan Rahatabad Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran 0000-0002-7340-9216 Hamid Azadeh Department of Physical Therapy, School of Rehabilitation Sciences, Isfahan University of Medical Sciences, Isfahan, Iran 0000-0003-0803-6844 Journal Article 2024 02 03 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.