Jin Su Kim¹, Min-Gu Kim¹, Jae Myung Kim^1,2, Sung Bum Pan^1,2,*

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2997-3007, 2022, DOI:10.32604/cmc.2022.026572

Abstract As fifth generation technology standard (5G) technology develops, the possibility of being exposed to the risk of cyber-attacks that exploits vulnerabilities in the 5G environment is increasing. The existing personal recognition method used for granting permission is a password-based method, which causes security problems. Therefore, personal recognition studies using bio-signals are being conducted as a method to access control to devices. Among bio-signal, surface electromyogram (sEMG) can solve the existing personal recognition problem that was unable to the modification of registered information owing to the characteristic changes in its signal according to the performed operation. Furthermore, as an advantage, sEMG… More >

Mai Ramadan Ibraheem¹, Jilan Adel², Alaa Eldin Balbaa³, Shaker El-Sappagh⁴, Tamer Abuhmed^5,*, Mohammed Elmogy⁶

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 393-409, 2021, DOI:10.32604/cmc.2021.014446

Abstract Surface electromyogram (sEMG) processing and classification can assist neurophysiological standardization and evaluation and provide habitational detection. The timing of muscle activation is critical in determining various medical conditions when looking at sEMG signals. Understanding muscle activation timing allows identification of muscle locations and feature validation for precise modeling. This work aims to develop a predictive model to investigate and interpret Patellofemoral (PF) osteoarthritis based on features extracted from the sEMG signal using pattern classification. To this end, sEMG signals were acquired from five core muscles over about 200 reads from healthy adult patients while they were going upstairs. Onset, offset,… More >