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Pose Estimation from Electromyographical Data using Convolutional Neural Networks

Ayling, Robin, Johnson, Colin G., Li, Ling, Palaniappan, Ramaswamy (2020) Pose Estimation from Electromyographical Data using Convolutional Neural Networks. In: 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). . pp. 653-656. IEEE ISBN 978-1-72811-991-5. E-ISBN 978-1-72811-990-8. (doi:10.1109/EMBC44109.2020.9175659) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:84610)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
https://doi.org/10.1109/EMBC44109.2020.9175659

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.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1109/EMBC44109.2020.9175659
Uncontrolled keywords: convolutional neural nets;electromyography;medical signal processing;pose estimation;signal classification;hand pose;Ninapro DB5 dataset;convolutional neural networks;electromyographical data;pose estimation;accelerometry data;EMG data;Electromyography;Solid modeling;Pose estimation;Muscles;Data models;Sensors;Gesture recognition;Electromyography;Accelerometry;Convolutional Neural Network;Pose Estimation;Accelerometry;Electromyography;Hand;Humans;Neural Networks, Computer
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Dr Ling Caroline Li
Date Deposited: 30 Nov 2020 01:57 UTC
Last Modified: 16 Feb 2021 14:16 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/84610 (The current URI for this page, for reference purposes)
Johnson, Colin G.: https://orcid.org/0000-0002-9236-6581
Li, Ling: https://orcid.org/0000-0002-4026-0216
Palaniappan, Ramaswamy: https://orcid.org/0000-0001-5296-8396
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