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<p>The prosthetic limb has been controlled by a surface EMG signal to protect the impaired person and allow them to execute their everyday functional movements. In this regard, myoelectric based control has demonstrated good performance in recent years when it comes to meeting the fundamental operational needs of disabled people and enhancing their quality of life. This paper’s objective is to carry out a comprehensive analysis and offer a thorough summary of the work done on prostheses and myoelectric interfaces. Numerous studies and works of literature have looked into and supported the regular application these signals in prostheses as a form of assistance technology. During this process, some of the primary documents were collected from different sources such as Frontiers, Hindwai, Scopus, Bio-Med Central, IntechOpen, Taylor a Francis, Springer, Pub Med and MDPI. Further, analysing the behaviour of analog and digital signals with the help of signal processing allows for better efficiency, feature extraction, pattern reorganization, and reconfiguration. The ongoing study used a systematic search strategy to identify literature that discussed cutting-edge surface EMG usage in prosthesis. Lastly this study has been made in an effort to explore the classifiers, feature transformation methods, various datasets and the extraction of feature methods for sEMG signals because it uses feature extraction methods to reveal hidden characteristic information in input signals. The suggested method is intended to extract the underlying patterns of muscle activation and inter-muscle coordination information from the sEMG signal, which are usually not extracted by the conventional feature extraction methods. This information is very important for prosthetic applications, as it helps to accurately distinguish hand motion patterns and provides smooth control of prosthetic devices. The findings of this investigation confirm the broad use of surface EMG in prostheses, and it is determined that surface EMG based prosthesis technology, yet in the early stages of development, needs extensive explorations.</p>

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A Systematic Analysis on Recent Trends of Surface Electromyography for Hand Recognition

  • 期刊论文

  • 发表日期：

    2026年6月3日

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  • Tanu Sharma,
  • K. P. Sharma,
  • Karan Veer

摘要

The prosthetic limb has been controlled by a surface EMG signal to protect the impaired person and allow them to execute their everyday functional movements. In this regard, myoelectric based control has demonstrated good performance in recent years when it comes to meeting the fundamental operational needs of disabled people and enhancing their quality of life. This paper’s objective is to carry out a comprehensive analysis and offer a thorough summary of the work done on prostheses and myoelectric interfaces. Numerous studies and works of literature have looked into and supported the regular application these signals in prostheses as a form of assistance technology. During this process, some of the primary documents were collected from different sources such as Frontiers, Hindwai, Scopus, Bio-Med Central, IntechOpen, Taylor a Francis, Springer, Pub Med and MDPI. Further, analysing the behaviour of analog and digital signals with the help of signal processing allows for better efficiency, feature extraction, pattern reorganization, and reconfiguration. The ongoing study used a systematic search strategy to identify literature that discussed cutting-edge surface EMG usage in prosthesis. Lastly this study has been made in an effort to explore the classifiers, feature transformation methods, various datasets and the extraction of feature methods for sEMG signals because it uses feature extraction methods to reveal hidden characteristic information in input signals. The suggested method is intended to extract the underlying patterns of muscle activation and inter-muscle coordination information from the sEMG signal, which are usually not extracted by the conventional feature extraction methods. This information is very important for prosthetic applications, as it helps to accurately distinguish hand motion patterns and provides smooth control of prosthetic devices. The findings of this investigation confirm the broad use of surface EMG in prostheses, and it is determined that surface EMG based prosthesis technology, yet in the early stages of development, needs extensive explorations.