About: A normalisation approach improves the performance of inter-subject sEMG-based hand gesture recognition with a ConvNet

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type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint http://eprints.org/ontology/EPrint Article Article Academic Article Academic Article
seeAlso	HTML Summary of #83026 A normalisation approach improves the performance of inter-subject sEMG-based hand gesture recognition with a ConvNet HTML Summary of #83026 A normalisation approach improves the performance of inter-subject sEMG-based hand gesture recognition with a ConvNet
sameAs	A normalisation approach improves the performance of inter-subject sEMG-based hand gesture recognition with a ConvNet A normalisation approach improves the performance of inter-subject sEMG-based hand gesture recognition with a ConvNet
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dc:hasVersion	A normalisation approach improves the performance of inter-subject sEMG-based hand gesture recognition with a ConvNet (PDF) A normalisation approach improves the performance of inter-subject sEMG-based hand gesture recognition with a ConvNet (PDF)
Title	A normalisation approach improves the performance of inter-subject sEMG-based hand gesture recognition with a ConvNet A normalisation approach improves the performance of inter-subject sEMG-based hand gesture recognition with a ConvNet
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2020-08-27 2020-08-27
Creator	L. Li L. Li Philippe De Wilde Philippe De Wilde Palaniappan Ramaswamy Palaniappan Ramaswamy Yuzhou Lin Yuzhou Lin
status	peer reviewed peer reviewed published published
Publisher	IEEE IEEE
abstract	Recently, the subject-specific surface electromyography (sEMG)-based gesture classification with deep learning algorithms has been widely researched. However, it is not practical to obtain the training data by requiring a user to perform hand gestures many times in real life. This problem can be alleviated to a certain extent if sEMG from many other subjects could be used to train the classifier. In this paper, we propose a normalisation approach that allows implementing real-time subject-independent sEMG based hand gesture classification without training the deep learning algorithm subject specifically. We hypothesed that the amplitude ranges of sEMG across channels between forearm muscle contractions for a hand gesture recorded in the same condition do not vary significantly within each individual. Therefore, the min-max normalisation is applied to source domain data but the new maximum and minimum values of each channel used to restrict the amplitude range are calculated from a trial cycle of a new user (target domain) and assigned by the class label. A convolutional neural network (ConvNet) trained with the normalised data achieved an average 87.03 accuracy on our G. dataset (12 gestures) and 94.53 on M. dataset (7 gestures) by using the leave-one-subject-out cross-validation. Recently, the subject-specific surface electromyography (sEMG)-based gesture classification with deep learning algorithms has been widely researched. However, it is not practical to obtain the training data by requiring a user to perform hand gestures many times in real life. This problem can be alleviated to a certain extent if sEMG from many other subjects could be used to train the classifier. In this paper, we propose a normalisation approach that allows implementing real-time subject-independent sEMG based hand gesture classification without training the deep learning algorithm subject specifically. We hypothesed that the amplitude ranges of sEMG across channels between forearm muscle contractions for a hand gesture recorded in the same condition do not vary significantly within each individual. Therefore, the min-max normalisation is applied to source domain data but the new maximum and minimum values of each channel used to restrict the amplitude range are calculated from a trial cycle of a new user (target domain) and assigned by the class label. A convolutional neural network (ConvNet) trained with the normalised data achieved an average 87.03 accuracy on our G. dataset (12 gestures) and 94.53 on M. dataset (7 gestures) by using the leave-one-subject-out cross-validation.
Is Part Of	https://kar.kent.ac.uk/id/repository https://kar.kent.ac.uk/id/repository
Subject	Q Science (General) Q Science (General)
list of authors	https://kar.kent.ac.uk/id/eprint/83026#authors https://kar.kent.ac.uk/id/eprint/83026#authors
presented at	2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC) 2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC)
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #83026 A normalisation approach improves the performance of inter-subject sEMG-based hand gesture recognition with a ConvNet HTML Summary of #83026 A normalisation approach improves the performance of inter-subject sEMG-based hand gesture recognition with a ConvNet

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