Li_ICAICA-CA158-Baao Xie.pdf (563.94 kB)
Download fileGesture recognition from bio-signals using hybrid deep neural networks
conference contribution
posted on 2020-09-21, 10:48 authored by Baao Xie, James Meng, Baihua LiBaihua Li, Andy HarlandAndy HarlandSurface electromyogram (sEMG) provides a
promising means to develop a non-invasive prosthesis control
system. In the context of transradial amputees, it allows a
limited but functionally useful return of hand function that can
significantly improve patients’ quality of life. In order to
predict users’ motion intention, the ability to process multichannel sEMG signals generated by muscle is required. We
propose an attention-based Bidirectional Convolutional Gated
Recurrent Unit (Bi-CGRU) deep neural network to analyse
sEMG signals. The two key novel aspects of our work include:
firstly, novel use of a bi-directional sequential GRU to focus on
the inter-channel relationship between both the prior time
steps and the posterior signals. This enhances the intra-channel
features extracted by an initial one-dimensional CNN.
Secondly, an attention component is employed at each GRU
layer. This mechanism learns different intra-attention weights,
enabling focus on vital parts and corresponding dependencies
of the signal. This increases robustness to feature noise to
further improve accuracy. The attention-based Bi-CGRU is
evaluated on the Ninapro benchmark dataset of sEMG hand
gestures. The electromyogram signals of 17 hand gestures from
10 subjects from the database are tested. The average accuracy
achieved 88.73%, outperforming the state-of-the-art
approaches on the same database. This demonstrates that the
proposed attention based Bi-CGRU model provides a
promising bio-control solution for robotic prostheses.
History
School
- Science
- Mechanical, Electrical and Manufacturing Engineering
Department
- Computer Science
Published in
2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)Pages
493 - 499Source
2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)Publisher
IEEEVersion
- AM (Accepted Manuscript)
Rights holder
© IEEEPublisher statement
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.Publication date
2020-09-01Copyright date
2020ISBN
9781728170053Publisher version
Language
- en