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Recent trends and challenges of surface electromyography in prosthetic applications

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Abstract

Surface electromyography (sEMG) meets extensive applications in the field of prosthesis in the current period. The effectiveness of sEMG in prosthesis applications has been verified by numerous revolutionary developments and extensive research attempts. A large volume of research and literature works have explored and validated the vast use of these signals in prostheses as an assistive technology. The objective of this paper is to conduct a systematic review and offer a detailed overview of the work record in the prosthesis and myoelectric interfaces framework. This review utilized a systematic search strategy to identify published articles discussing the state-of-the-art applications of sEMG in prostheses (including upper limb prosthesis and lower limb prostheses). Relevant studies were identified using electronic databases such as PubMed, IEEE Explore, SCOPUS, ScienceDirect, Google Scholar and Web of Science. Out of 3791 studies retrieved from the databases, 188 articles were found to be potentially relevant (after screening of abstracts and application of inclusion–exclusion criteria) and included in this review. This review presents an investigative analysis of sEMG-based prosthetic applications to assist the readers in making further advancements in this field. It also discusses the fundamental advantages and disadvantages of using sEMG in prosthetic applications. It also includes some important guidelines to follow in order to improve the performance of sEMG-based prosthesis. The findings of this study support the widespread use of sEMG in prosthetics. It is concluded that sEMG-based prosthesis technology, still in its sprouting phase, requires significant explorations for further development. Supplementary investigations are necessary in the direction of making a seamless mechanism of biomechatronics for sEMG-based prosthesis by cohesive efforts of robotic researchers and biomedical engineers.

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Notes

  1. Triwiyanto, T., Caesarendra, W., Purnomo, M.H., Sułowicz, M., Wisana, I.D.G.H., Titisari, D., Lamidi, L. and Rismayani, R., 2022. Embedded machine learning using a multi-thread algorithm on a Raspberry Pi platform to improve prosthetic hand performance. Micromachines, 13(2), p.191.

  2. Burhan, N., & Ghazali, R. (2016, October). Feature extraction of surface electromyography (sEMG) and signal processing technique in wavelet transform: A review. In 2016 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS) (pp. 141–146). IEEE.

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    Drishti Yadav & Karan Veer

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Yadav, D., Veer, K. Recent trends and challenges of surface electromyography in prosthetic applications. Biomed. Eng. Lett. 13, 353–373 (2023). https://doi.org/10.1007/s13534-023-00281-z

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