Jin Su Kim, Sungbum Pan^*

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1663-1678, 2023, DOI:10.32604/cmc.2023.043998

Abstract As Internet of Things (IoT) technology develops, integrating network functions into diverse equipment introduces new challenges, particularly in dealing with counterfeit issues. Over the past few decades, research efforts have focused on leveraging electromyogram (EMG) for personal recognition, aiming to address security concerns. However, obtaining consistent EMG signals from the same individual is inherently challenging, resulting in data irregularity issues and consequently decreasing the accuracy of personal recognition. Notably, conventional studies in EMG-based personal recognition have overlooked the issue of data irregularities. This paper proposes an innovative approach to personal recognition that combines a siamese fusion network with an auxiliary… More >

Mahdi Souzanchi-K¹, Mohammad-R Akbarzadeh-T^1,*, Nadia Naghavi¹, Ali Sharifnezhad², Vahab Khoshdel³

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 279-294, 2022, DOI:10.32604/iasc.2022.023717

Abstract Sliding mode control is often used for systems with parametric uncertainties due to its desirable robustness and stability, but this approach carries undesirable chattering. Similarly, joint elasticity is a common phenomenon induced by transmission systems in robots, but it presents additional complexity in robot dynamics that could lead to robot vibrations or even instability. Coupling these two phenomena presents further compounded challenges, particularly when faced with the human interface's added uncertainties. Here, a stable voltage-based adaptive fuzzy strategy to sliding mode control is proposed for an elastic joint robot arm that uses a human's upper limb electromyogram (EMG) signals to… More >

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 >