Recurrent Fusion of Time-Domain Descriptors Improves EMG-based Hand Movement Recognition.

Al Taee, AA; Khushaba, RN; Al-Timemy, AH; Al-Jumaily, A

Recurrent Fusion of Time-Domain Descriptors Improves EMG-based Hand Movement Recognition.

Al Taee, AA Khushaba, RN Al-Timemy, AH Al-Jumaily, A

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2020, 2020, pp. 657-661
Issue Date:: 2020-07

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Field	Value	Language
dc.contributor.author	Al Taee, AA
dc.contributor.author	Khushaba, RN
dc.contributor.author	Al-Timemy, AH
dc.contributor.author	Al-Jumaily, A
dc.date	2020-07-20
dc.date.accessioned	2021-06-16T07:15:26Z
dc.date.available	2021-06-16T07:15:26Z
dc.date.issued	2020-07
dc.identifier.citation	Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2020, 2020, pp. 657-661
dc.identifier.isbn	9781728119908
dc.identifier.issn	2375-7477
dc.identifier.issn	2694-0604
dc.identifier.uri	http://hdl.handle.net/10453/149559
dc.description.abstract	Controlling powered prostheses with myoelectric pattern recognition (PR) provides a natural human-robot interfacing scheme for amputees who lost their limbs. Research in this direction reveals that the challenges prohibiting reliable clinical translation of myoelectric interfaces are mainly driven by the quality of the extracted features. Hence, developing accurate and reliable feature extraction techniques is of vital importance for facilitating clinical implementation of Electromyogram (EMG) PR systems. To overcome this challenge, we proposed a combination of Range Spatial Filtering (RSF) and Recurrent Fusion of Time Domain Descriptors (RFTDD) in order to improve the classifier performance and make the prosthetic hand control more appropriate for clinical applications. RSF is used to increase the number of EMG signals available for feature extraction by focusing on the spatial information between all possible logical combinations of the physical EMG channels. RFTDD is then used to capture the temporal information by applying a recurrent data fusion process on the resulting orientation-based time-domain (TD) features, with a sigmoidal function to limit the features range and overcome the vanishing amplitudes problem. The main advantages of the proposed method include 1) its potential in capturing the temporal-spatial dependencies of the EMG signals, leading to reduced classification errors, and 2) the simplicity with which the features are extracted, as any kind of simple TD features can be adopted with this method. The performance of the proposed RFTDD is then benchmarked across many well-known TD features individually and as sets to prove the power of the RFTDD method on two EMG datasets with a total of 31 subjects. Testing results revealed an approximate reduction of 12% in classification errors across all subjects when using the proposed method against traditional feature extraction methods.Clinical Relevance-Establishing significance and importance of RFTDD, with simple time-domain features, for robust and low-cost clinical applications.
dc.format	Print
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
dc.relation.ispartof	2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society
dc.relation.isbasedon	10.1109/embc44109.2020.9175602
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.mesh	Hand
dc.subject.mesh	Humans
dc.subject.mesh	Electromyography
dc.subject.mesh	Artificial Limbs
dc.subject.mesh	Movement
dc.subject.mesh	Algorithms
dc.subject.mesh	Algorithms
dc.subject.mesh	Artificial Limbs
dc.subject.mesh	Electromyography
dc.subject.mesh	Hand
dc.subject.mesh	Humans
dc.subject.mesh	Movement
dc.title	Recurrent Fusion of Time-Domain Descriptors Improves EMG-based Hand Movement Recognition.
dc.type	Conference Proceeding
utslib.citation.volume	2020
utslib.location.activity	United States
utslib.copyright.status	closed_access	*
dc.date.updated	2021-06-16T07:15:25Z
pubs.finish-date	2020-07-24
pubs.publication-status	Published
pubs.start-date	2020-07-20
pubs.volume	2020

Abstract:

Controlling powered prostheses with myoelectric pattern recognition (PR) provides a natural human-robot interfacing scheme for amputees who lost their limbs. Research in this direction reveals that the challenges prohibiting reliable clinical translation of myoelectric interfaces are mainly driven by the quality of the extracted features. Hence, developing accurate and reliable feature extraction techniques is of vital importance for facilitating clinical implementation of Electromyogram (EMG) PR systems. To overcome this challenge, we proposed a combination of Range Spatial Filtering (RSF) and Recurrent Fusion of Time Domain Descriptors (RFTDD) in order to improve the classifier performance and make the prosthetic hand control more appropriate for clinical applications. RSF is used to increase the number of EMG signals available for feature extraction by focusing on the spatial information between all possible logical combinations of the physical EMG channels. RFTDD is then used to capture the temporal information by applying a recurrent data fusion process on the resulting orientation-based time-domain (TD) features, with a sigmoidal function to limit the features range and overcome the vanishing amplitudes problem. The main advantages of the proposed method include 1) its potential in capturing the temporal-spatial dependencies of the EMG signals, leading to reduced classification errors, and 2) the simplicity with which the features are extracted, as any kind of simple TD features can be adopted with this method. The performance of the proposed RFTDD is then benchmarked across many well-known TD features individually and as sets to prove the power of the RFTDD method on two EMG datasets with a total of 31 subjects. Testing results revealed an approximate reduction of 12% in classification errors across all subjects when using the proposed method against traditional feature extraction methods.Clinical Relevance-Establishing significance and importance of RFTDD, with simple time-domain features, for robust and low-cost clinical applications.

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