Review
Version 1
Preserved in Portico This version is not peer-reviewed
Controlling Upper Limb Prostheses Using Sonomyography (SMG): A Review
Version 1
: Received: 12 January 2023 / Approved: 23 January 2023 / Online: 23 January 2023 (01:43:05 CET)
A peer-reviewed article of this Preprint also exists.
Nazari, V.; Zheng, Y.-P. Controlling Upper Limb Prostheses Using Sonomyography (SMG): A Review. Sensors 2023, 23, 1885. Nazari, V.; Zheng, Y.-P. Controlling Upper Limb Prostheses Using Sonomyography (SMG): A Review. Sensors 2023, 23, 1885.
Abstract
This paper presents a critical review and comparison of the results of recently published studies in the fields of human-machine interface and the use of sonomyography (SMG) for the control of upper limb prothesis. For this review paper, a combination of the keywords "Human Machine Interface", "Sonomyography", "Ultrasound", "Upper Limb Prosthesis", "Artificial Intelligence" and "Non-Invasive Sensors" was used to search for articles on Google Scholar and PubMed. Sixty-one articles were found, of which 59 were used in this review. For a comparison of the different ultrasound modes, feature extraction methods, and machine learning algorithms, 16 articles were used. It was found that various modes of ultrasound devices for prosthetic control, various machine learning algorithms for classifying different hand gestures, as well as various feature extraction methods for increasing the accuracy of artificial intelligence used in their controlling systems are reviewed in this article. The results of the review article show that ultrasound sensing has the potential to be used as a viable human-machine interface in order to control bionic hands with multiple degrees of freedom. Moreover, different hand gestures can be classified by different machine learning algorithms trained with extracted features from collected data with an accuracy of around 95%.
Keywords
Controlling system; human machine interface; machine learning; non-invasive sensor; prosthesis, sonomyography.
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment
