Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105355
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Biomedical Engineering | - |
dc.contributor | Research Institute for Smart Ageing | - |
dc.creator | Nazari, V | - |
dc.creator | Zheng, YP | - |
dc.date.accessioned | 2024-04-12T06:51:54Z | - |
dc.date.available | 2024-04-12T06:51:54Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/105355 | - |
dc.language.iso | en | en_US |
dc.publisher | Molecular Diversity Preservation International (MDPI) | en_US |
dc.rights | © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | The following publication Nazari V, Zheng Y-P. Controlling Upper Limb Prostheses Using Sonomyography (SMG): A Review. Sensors. 2023; 23(4):1885 is available at https://doi.org/10.3390/s23041885. | en_US |
dc.subject | Controlling system | en_US |
dc.subject | Human–machine interface | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Non-invasive sensor | en_US |
dc.subject | Prosthesis | en_US |
dc.subject | Sonomyography | en_US |
dc.title | Controlling upper limb prostheses using sonomyography (SMG) : a review | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 23 | - |
dc.identifier.issue | 4 | - |
dc.identifier.doi | 10.3390/s23041885 | - |
dcterms.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 fifty-nine were used in this review. For a comparison of the different ultrasound modes, feature extraction methods, and machine learning algorithms, 16 articles were used. Various modes of ultrasound devices for prosthetic control, various machine learning algorithms for classifying different hand gestures, and 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%. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Sensors, Feb. 2023, v. 23, no. 4, 1885 | - |
dcterms.isPartOf | Sensors | - |
dcterms.issued | 2023-02 | - |
dc.identifier.scopus | 2-s2.0-85148971286 | - |
dc.identifier.pmid | 36850483 | - |
dc.identifier.eissn | 1424-8220 | - |
dc.identifier.artn | 1885 | - |
dc.description.validate | 202403 bcvc | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | en_US |
dc.description.fundingSource | others | en_US |
dc.description.fundingText | Telefield Charitable Fund | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
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File | Description | Size | Format | |
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sensors-23-01885.pdf | 1.84 MB | Adobe PDF | View/Open |
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