TY - JOUR ID - 131148 TI - Modeling the Electromyogram Signal of Stimulated Biceps Brachii Muscle JO - International Journal of Medical Reviews JA - IJMR LA - en SN - 2345-525X AU - khodadadi, Vahid AU - Rahatabad, Fereidoun Nowshiravan AU - Sheikhani, Ali AU - Jafarnia Dabanloo, Nader AD - Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran AD - Engineering Research Center in Medicine and Biology, Science and Research Branch, Islamic Azad University, Tehran, Iran Y1 - 2021 PY - 2021 VL - 8 IS - 3 SP - 140 EP - 145 KW - Modeling KW - signal KW - Electromyogram KW - Biceps Brachii Muscle DO - 10.30491/ijmr.2020.253244.1151 N2 -  Introduction: The usage of modeling methods has been proposed to achieve a better understanding of biological systems, so that some ambiguities about their function could be resolved. Accordingly, the present review was performed to model the electromyogram signal of stimulated biceps brachii muscle.Methods: In this review study, a search was performed in databases of Emerald, Cochrane Library, MEEDLINE, EMBASE, Wiley, Scopus, and Magiran on papers published over the past 20 years. Papers that fulfilled all inclusion criteria were critically appraised in order to assess their quality. Out of the 66 papers extracted, eight original papers were included. The findings obtained from the papers were noted, and then underwent content analysis and categorization.Results: Findings indicated that most of the performed studies had been modeled using cybernetic, robotic, regression, and neural network modeling methods. These physiological mathematical models model the physiological structure of the muscle based on a direct description of biomechanical, biological, and physiological characteristics of the system individually, which is difficult for obtaining many parameters.Conclusion: Most of the models presented so far do not match reality and have errors. Thus, studies are required to design a model similar to a biological system with the properties of biological systems in order to reduce the modeling error.  UR - https://www.ijmedrev.com/article_131148.html L1 - https://www.ijmedrev.com/article_131148_17d1da481e3f937d9936fbe58c5be204.pdf ER -