About: Pose Estimation from Electromyographical Data using Convolutional Neural Networks

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type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #84610 Pose Estimation from Electromyographical Data using Convolutional Neural Networks
sameAs	Pose Estimation from Electromyographical Data using Convolutional Neural Networks
Title	Pose Estimation from Electromyographical Data using Convolutional Neural Networks
described by	https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2020-04-10
Creator	Robin Ayling Colin G. Johnson L. Li Palaniappan Ramaswamy
status	peer reviewed published
Publisher	IEEE
abstract	This work demonstrates the effectiveness of Convolutional Neural Networks in the task of pose estimation from Electromyographical (EMG) data. The Ninapro DB5 dataset was used to train the model to predict the hand pose from EMG data. The models predict the hand pose with an error rate of 4.6 for the EMG model, and 3.6 when accelerometry data is included. This shows that hand pose can be effectively estimated from EMG data, which can be enhanced with accelerometry data.
Is Part Of	2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) https://kar.kent.ac.uk/id/repository
Subject	Q335 Artificial intelligence
list of authors	https://kar.kent.ac.uk/id/eprint/84610#authors
presented at	2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC)
is topic of	https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #84610 Pose Estimation from Electromyographical Data using Convolutional Neural Networks

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