Spatially Filtered Low-Density EMG and Time-Domain Descriptors Improves Hand Movement Recognition.
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2019, 2019, pp. 2671-2674
- Issue Date:
- 2019-07
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Field | Value | Language |
---|---|---|
dc.contributor.author | Al Taee, AA | |
dc.contributor.author | Khushaba, RN | |
dc.contributor.author |
Al-Jumaily, A |
|
dc.date | 2019-07-23 | |
dc.date.accessioned | 2020-05-15T23:10:09Z | |
dc.date.available | 2021-08-12T19:00:45Z | |
dc.date.issued | 2019-07 | |
dc.identifier.citation | Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2019, 2019, pp. 2671-2674 | |
dc.identifier.isbn | 978-1-5386-1311-5 | |
dc.identifier.issn | 1557-170X | |
dc.identifier.uri | http://hdl.handle.net/10453/140749 | |
dc.description.abstract | Surface Electromyogram (EMG) pattern recognition has long been utilized for controlling multifunctional myoelectric prostheses. In such an application, a number of EMG channels are usually utilized to acquire more information about the underlying activity of the remaining muscles in the amputee stump. However, despite the multichannel nature of this application, the extracted features are usually acquired from each channel individually, without consideration for the interaction between the different muscles recruited to achieve a specific movement. In this paper, we proposed an approach of spatial filtering, denoted as Range Spatial Filtering (RSF), to increase the number of EMG channels available for feature extraction, by considering the range of all possible logical combinations of each n channels. The proposed RSF method is then combined with conventional time-domain (TD) feature extraction, as an extension of the conventional single channel TD features that are heavily considered in this field. We then show how the addition of a new feature, specifically the minimum absolute value of the range of each two windowed EMG signals, can significantly reduce the different patterns misclassification rate achieved by conventional TD features (with and without our RSF method). The performance of the proposed method is verified on EMG data collected from nine transradial amputees (seven traumatic and two congenital), with six grip and finger movements, for three different levels of forces (low, medium, and high). The classification results showed significant reduction in classification error rates compared to other methods (nearly 10% for some individual TD features and 5% for combined TD features, with Bonferroni corrected p-values <; 0.01). | |
dc.format | ||
dc.language | en | |
dc.publisher | IEEE | |
dc.relation.ispartof | Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference | |
dc.relation.ispartof | Annual International Conference of the IEEE Engineering in Medicine and Biology Society | |
dc.relation.isbasedon | 10.1109/embc.2019.8857289 | |
dc.rights | info:eu-repo/semantics/embargoedAccess | |
dc.rights | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.subject.mesh | Hand | |
dc.subject.mesh | Fingers | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Electromyography | |
dc.subject.mesh | Prosthesis Design | |
dc.subject.mesh | Movement | |
dc.subject.mesh | Algorithms | |
dc.subject.mesh | Pattern Recognition, Automated | |
dc.subject.mesh | Amputees | |
dc.subject.mesh | Hand | |
dc.subject.mesh | Fingers | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Electromyography | |
dc.subject.mesh | Prosthesis Design | |
dc.subject.mesh | Movement | |
dc.subject.mesh | Algorithms | |
dc.subject.mesh | Pattern Recognition, Automated | |
dc.subject.mesh | Amputees | |
dc.subject.mesh | Algorithms | |
dc.subject.mesh | Amputees | |
dc.subject.mesh | Electromyography | |
dc.subject.mesh | Fingers | |
dc.subject.mesh | Hand | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Movement | |
dc.subject.mesh | Pattern Recognition, Automated | |
dc.subject.mesh | Prosthesis Design | |
dc.title | Spatially Filtered Low-Density EMG and Time-Domain Descriptors Improves Hand Movement Recognition. | |
dc.type | Conference Proceeding | |
utslib.citation.volume | 2019 | |
utslib.location.activity | Berlin, Germany | |
pubs.organisational-group | /University of Technology Sydney/Faculty of Engineering and Information Technology | |
pubs.organisational-group | /University of Technology Sydney/Strength - CHT - Health Technologies | |
pubs.organisational-group | /University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering | |
pubs.organisational-group | /University of Technology Sydney | |
pubs.organisational-group | /University of Technology Sydney/Strength - CTWW - Centre for Technology in Water and Wastewater Treatment | |
pubs.organisational-group | /University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering | |
utslib.copyright.status | open_access | * |
pubs.consider-herdc | false | |
dc.date.updated | 2020-05-15T23:09:56Z | |
pubs.finish-date | 2019-07-27 | |
pubs.place-of-publication | Piscataway, USA | |
pubs.publication-status | Published | |
pubs.start-date | 2019-07-23 | |
pubs.volume | 2019 | |
utslib.start-page | 2671 | |
dc.location | Piscataway, USA |
Abstract:
Surface Electromyogram (EMG) pattern recognition has long been utilized for controlling multifunctional myoelectric prostheses. In such an application, a number of EMG channels are usually utilized to acquire more information about the underlying activity of the remaining muscles in the amputee stump. However, despite the multichannel nature of this application, the extracted features are usually acquired from each channel individually, without consideration for the interaction between the different muscles recruited to achieve a specific movement. In this paper, we proposed an approach of spatial filtering, denoted as Range Spatial Filtering (RSF), to increase the number of EMG channels available for feature extraction, by considering the range of all possible logical combinations of each n channels. The proposed RSF method is then combined with conventional time-domain (TD) feature extraction, as an extension of the conventional single channel TD features that are heavily considered in this field. We then show how the addition of a new feature, specifically the minimum absolute value of the range of each two windowed EMG signals, can significantly reduce the different patterns misclassification rate achieved by conventional TD features (with and without our RSF method). The performance of the proposed method is verified on EMG data collected from nine transradial amputees (seven traumatic and two congenital), with six grip and finger movements, for three different levels of forces (low, medium, and high). The classification results showed significant reduction in classification error rates compared to other methods (nearly 10% for some individual TD features and 5% for combined TD features, with Bonferroni corrected p-values <; 0.01).
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